6638 lines
370 KiB
HTML
6638 lines
370 KiB
HTML
<div data-align="center">
|
||
<pre><code><img width="400" height="253" src="assets/abd_map.png" alt="Roadmap of studying Abduction"></code></pre>
|
||
</div>
|
||
<h1
|
||
id="awesome-artificial-general-intelligence-and-computational-cognitive-sciences-awesome">Awesome
|
||
Artificial General Intelligence and Computational Cognitive Sciences <a
|
||
href="https://awesome.re"><img src="https://awesome.re/badge.svg"
|
||
alt="Awesome" /></a></h1>
|
||
<p>An <strong>awesome & curated</strong> list for <strong>Artificial
|
||
General Intelligence</strong>, an emerging inter-discipline field that
|
||
combines artificial intelligence and computational cognitive sciences as
|
||
majority, alone with probability and statistics, formal logic, cognitive
|
||
and developmental psychology, computational philosophy, cognitive
|
||
neuroscience, and computational sociology. We are promoting high-level
|
||
machine intelligence by getting inspirations from the way that human
|
||
learns and thinks, while obtaining a deeper understanding of human
|
||
cognition simultaneously. We believe that this kind of reciprocative
|
||
research is a potential way towards our big picture: building
|
||
human-level intelligent systems with capabilities such as abstracting,
|
||
explaining, learning, planning, and making decisions. And such
|
||
intelligence may generally help people improve scientific research,
|
||
engineering, and the arts, which are the hallmarks of human
|
||
intelligence.</p>
|
||
<p><strong><em>Awesome AGI & CoCoSci</em></strong> is an all-in-one
|
||
collection, consisting of recources from basic courses and tutorials, to
|
||
papers and books around diverse topics in mutiple perspectives. Both
|
||
junior and senior researchers, whether learning, working on, or working
|
||
around AGI and CoCoSci, meet their interest here.</p>
|
||
<h2 id="contributing">Contributing</h2>
|
||
<p>Contributions are greatly welcomed! Please refer to <a
|
||
href="Contributing.md">Contribution Guidelines</a> before taking any
|
||
action.</p>
|
||
<p><span id="c"></span> ## Contents * <a href="#academic-tools">Academic
|
||
Tools</a> * <a href="#courses">Courses</a> * <a
|
||
href="#programming">Programming</a> * <a href="#paper-writing">Paper
|
||
Writing</a> * <a href="#paper-reading">Paper Reading</a> * <a
|
||
href="#literature-management">Literature Management</a> * <a
|
||
href="#knowledge-management">Knowledge Management</a> * <a
|
||
href="#papers">Papers</a> * <a href="#abduction">Abduction</a> * <a
|
||
href="#explanation">Explanation</a> * <a
|
||
href="#scientific-discovery">Scientific Discovery</a> * <a
|
||
href="#rationalization">Rationalization</a> * <a
|
||
href="#applications-in-ai">Applications in AI</a> * <a
|
||
href="#bayesian-modeling">Bayesian Modeling</a> * <a
|
||
href="#bayesian-induction">Bayesian Induction</a> * <a
|
||
href="#generative-model">Generative Model</a> * <a
|
||
href="#nonparametric-model">Nonparametric Model</a> * <a
|
||
href="#bayesian-optimization">Bayesian Optimization</a> * <a
|
||
href="#concepts">Concepts</a> * <a href="#theory-of-concepts">Theory of
|
||
Concepts</a> * <a href="#human-concept-representation">Human Concept
|
||
Represenataion</a> * <a href="#ai-concept-representation">AI Concept
|
||
Representation</a> * <a
|
||
href="#complexity--information-theory">Complexity & Information
|
||
Theory</a> * <a href="#theory">Theory</a> * <a
|
||
href="#dimensionality-reduction">Dimensionality Reduction</a> * <a
|
||
href="#visual-complexity">Visual Complexity</a> * <a
|
||
href="#communications">Communications</a> * <a
|
||
href="#non-verbal-communication">Non-Verbal Communication</a> * <a
|
||
href="#pragmatics">Pragmatics</a> * <a
|
||
href="#language-compositionality">Language Compositionality</a> * <a
|
||
href="#coordination">Coordination</a> * <a
|
||
href="#domain-specific-language">Domain Specific Language</a> * <a
|
||
href="#design-theory">Design Theory</a> * <a
|
||
href="#design-practises">Design Practises</a> * <a
|
||
href="#domain-specified-applications">Domain Specified Applications</a>
|
||
* <a href="#dsl-program-synthesis">DSL Program Synthesis</a> * <a
|
||
href="#problem-solving">Problem Solving</a> * <a
|
||
href="#human-level-problem-solving">Human-Level Problem Solving</a> * <a
|
||
href="#planning">Planning</a> * <a
|
||
href="#intrinsic-motivation">Intrinsic Motivation</a> * <a
|
||
href="#reinforcement-learning">Reinforcement Learning</a> * <a
|
||
href="#inverse-reinforcement-learning">Inverse Reinforcement
|
||
Learning</a> * <a href="#system-1--system-2">System 1 & System 2</a>
|
||
* <a href="#dual-coding-theory">Dual-Coding Theory</a> * <a
|
||
href="#neural-symbolic-ai">Neural-Symbolic AI</a> * <a
|
||
href="#explainability">Explainability</a> * <a
|
||
href="#trustworthy-ai">Trustworthy AI</a> * <a
|
||
href="#strong-machine-learning">Strong Machine Learning</a> * <a
|
||
href="#explainable-deep-learning">Explainable Deep Learning</a> * <a
|
||
href="#embodied-intelligence">Embodied Intelligence</a> * <a
|
||
href="#evolutionary-intelligence">Evolutionary Intelligence</a> * <a
|
||
href="#methodologies-for-experiments">Methodologies for Experiments</a>
|
||
* <a href="#quantitative-analysis">Quantitative Analysis</a> * <a
|
||
href="#scaling-up-behavioral-studies">Scaling Up Behavioral Studies</a>
|
||
* <a href="#decision-making">Decision Making</a> * <a
|
||
href="#question-answering">Question Answering</a> * <a
|
||
href="#human-machine-comparison">Human-Machine Comparison</a> * <a
|
||
href="#association-test">Association Test</a> * <a
|
||
href="#virtual-reality">Virtual Reality</a> * <a
|
||
href="#meta-level-considerations">Meta-Level Considerations</a> * <a
|
||
href="#meta-learning">Meta Learning</a> * <a
|
||
href="#marrs-levels-of-analysis">Marr’s Levels of Analysis</a> * <a
|
||
href="#gestalt">Gestalt</a> * <a href="#the-aha-moment">The Aha!
|
||
Moment</a> * <a href="#rationality">Rationality</a> * <a
|
||
href="#cognitive-architecture">Cognitive Architecture</a> * <a
|
||
href="#science-logology">Science Logology</a> * <a
|
||
href="#philosophy-of-science">Philosophy of Science</a> * <a
|
||
href="#science-of-science">Science of Science</a> * <a
|
||
href="#literature-mining">Literature Mining</a> * <a
|
||
href="#literature-visualization">Literature Visualization</a> * <a
|
||
href="#scientific-writing">Scientific Writing</a> * <a
|
||
href="#science-education">Science Education</a> * <a
|
||
href="#democratization-of-science">Democratization of Science</a> * <a
|
||
href="#laboratory-automation">Laboratory Automation</a> * <a
|
||
href="#theory-of-mind">Theory of Mind</a> * <a
|
||
href="#analogy">Analogy</a> * <a href="#causality">Causality</a> * <a
|
||
href="#commonsense">Commonsense</a> * <a
|
||
href="#intuitive-physics">Intuitive Physics</a> * <a
|
||
href="#ai-commonsense-reasoning">AI Commonsense Reasoning</a> * <a
|
||
href="#commonsense-knowledgebase">Commonsense Knowledgebase</a> * <a
|
||
href="#inductive-logic--program-synthesis">Inductive Logic & Program
|
||
Synthesis</a> * <a href="#knowledge-representation">Knowledge
|
||
Representation</a> * <a href="#cognitive-development">Cognitive
|
||
Development</a> * <a href="#learning-in-the-open-world">Learning in the
|
||
Open World</a> * <a
|
||
href="#learning-with-cognitive-plausibility">Learning with Cognitive
|
||
Plausibility</a> <!--* [Tasks & Environments](#te)--> * <a
|
||
href="#institute--researcher">Institute & Researcher</a> * <a
|
||
href="#mit">MIT</a> * <a href="#stanford">Stanford</a> * <a
|
||
href="#princeton">Princeton</a> * <a href="#harvard">Harvard</a> * <a
|
||
href="#ucla">UCLA</a> * <a href="#uc-berkeley">UC Berkeley</a> * <a
|
||
href="#bnu">BNU</a> * <a href="#pku">PKU</a> * <a href="#ucsd">UCSD</a>
|
||
* <a href="#nyu">NYU</a> * <a href="#jhu">JHU</a> * <a
|
||
href="#sit">SIT</a> * <a href="#people--book">People & Book</a> * <a
|
||
href="#ulf-grenander">Ulf Grenander</a> * <a href="#david-marr">David
|
||
Marr</a> * <a href="#michael-tomasello">Michael Tomasello</a> * <a
|
||
href="#judea-pearl">Judea Pearl</a> * <a href="#susan-carey">Susan
|
||
Carey</a> * <a href="#daniel-kahneman">Daniel Kahneman</a> * <a
|
||
href="#karl-popper">Karl Popper</a> * <a href="#john-hopcroft">John
|
||
Hopcroft</a> * <a href="#about">About</a></p>
|
||
<h2 id="academic-tools">Academic Tools</h2>
|
||
<h3 id="courses">Courses</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://cbmm.mit.edu/education/courses/computational-cognitive-science">Computational
|
||
Cognitive Science Courses</a> - <strong><em>MIT</em></strong>. Courses
|
||
on computational cognitive science from MIT, Harvard, and
|
||
Stanford.</p></li>
|
||
<li><p><a
|
||
href="https://people.csail.mit.edu/asolar/SynthesisCourse/index.htm">Introduction
|
||
to Program Synthesis</a> - <strong><em>MIT</em></strong>. Armando
|
||
Solar-Lezama’s elementary course on program synthesis.</p></li>
|
||
<li><p><a href="https://web.mit.edu/6.001/6.037/">Structure and
|
||
Interpretation of Computer Programs</a> - <strong><em>MIT</em></strong>.
|
||
[<a href="https://web.mit.edu/6.001/6.037/sicp.pdf">Book: SICP</a>]. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7488066943428166450&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Classic course on applying structural, procedural, and
|
||
meta-linguistic abstraction to solve computational problems.</p></li>
|
||
<li><p><a
|
||
href="https://faculty.ksu.edu.sa/sites/default/files/rosen_discrete_mathematics_and_its_applications_7th_edition.pdf">Discrete
|
||
Mathematics and Its Applications</a>. Classic course on basic discrete
|
||
mathematics, including matheatical logic, set theory, graph theory,
|
||
formal language (and automata), basic number theory (e.g., counting),
|
||
and other related topics.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="programming">Programming</h3>
|
||
<ul>
|
||
<li><a href="https://probmods.org/">Probabilistic Models of
|
||
Cognition</a> - <strong><em>MIT</em></strong>. The probabilistic
|
||
approach to cognitive science, which models learning and reasoning as
|
||
inference in complex probabilistic models.</li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="paper-writing">Paper Writing</h3>
|
||
<ul>
|
||
<li><p><a href="LaTex/config.sty">LaTex Configuration</a> -
|
||
<strong><em>LaTex</em></strong>. LaTex template for configuration file
|
||
with elegant reference style (gray-colored reference, page backward
|
||
reference).</p></li>
|
||
<li><p><a href="BibTex/references_header.bib">BibTex Template</a> -
|
||
<strong><em>BibTex</em></strong>. BibTex template for including
|
||
abbreviations of journals and conferences in AI, Mathematics, and
|
||
Cognitive Sciences.</p></li>
|
||
<li><p><a href="https://www.biorender.com/">bioRender</a> -
|
||
<strong><em>bioRender</em></strong>. Create professional science figures
|
||
in minutes by browsing thousands of pre-made icons and templates from
|
||
more than 30 fields of life sciences.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/documents/nature-summary-paragraph.pdf">How
|
||
to construct a Nature summary paragraph</a> -
|
||
<strong><em>Nature</em></strong>. Nature official guidelines for
|
||
composing abstracts.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/d41586-020-03422-x">How
|
||
to write a superb literature review</a> -
|
||
<strong><em>Nature</em></strong>, 2020. Nature speaks to old hands and
|
||
first timers about the work they did to make their reviews
|
||
sing.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/scitable/topicpage/scientific-papers-13815490/">Scientific
|
||
Papers</a> - <strong><em>Nature</em></strong>. Nature guidance on
|
||
writing scientific papers.</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf">The
|
||
Machine Learning Reproducibility Checklist</a> - <strong><em>McGill
|
||
University</em></strong>. Guidelines for introducing a machine learning
|
||
algorithm with guarantee of reproducibility.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="paper-reading">Paper Reading</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.cs.uni-potsdam.de/bs/teaching/docs/courses/ss2020/scn/material/p83-keshavA.pdf">How
|
||
to Read a Paper</a> - <strong><em>ACM SIGCOMM Computer Communication
|
||
Review</em></strong>, 2007. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7234542241721187587&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive tutorial on reading scientific
|
||
papers.</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/content/article/how-seriously-read-scientific-paper">How
|
||
to (seriously) read a scientific paper</a> -
|
||
<strong><em>Science</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=How+to+%28seriously%29+read+a+scientific+paper&btnG=">All
|
||
Versions</a>]. Science interview on reading scientific papers.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/nature.2017.21751">It’s
|
||
not just you: science papers are getting harder to read</a> -
|
||
<strong><em>Nature</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Nature perspective on reading scientific papers.</p></li>
|
||
<li><p><a
|
||
href="https://be.mit.edu/sites/default/files/documents/HowToReadAScientificPaper.pdf">How
|
||
to navigate a scientific paper with time constraints: a graphics
|
||
approach</a> - <strong><em>MIT</em></strong>. MIT guidance on strategies
|
||
for reading papers given different time constraints.</p></li>
|
||
<li><p><a href="https://textvis.lnu.se/">Text Visualization Browser</a>
|
||
- <strong><em>ISOVIS group</em></strong>, 2015. [<a
|
||
href="https://cs.lnu.se/isovis/pubs/docs/kucher-pacificvis15-postprint.pdf">Paper</a>].
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=7000995325728444282&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A Hub of Text Visualization Techniques.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="literature-management">Literature Management</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.science.org/content/article/how-keep-scientific-literature">How
|
||
to keep up with the scientific literature</a> -
|
||
<strong><em>Science</em></strong>, 2016. Science interview on organizing
|
||
scientific papers.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/nj7612-457a">Scientific
|
||
literature: Information overload</a> - <strong><em>Nature</em></strong>,
|
||
2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Perspective on handling overloaded information from
|
||
scientific literature.</p></li>
|
||
<li><p><a
|
||
href="https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/">Microsoft
|
||
Academic Graph</a> - <strong><em>Microsoft Research</em></strong>.
|
||
Heterogeneous graph containing scientific publication records, citation
|
||
relationships between those publications, as well as authors,
|
||
institutions, journals, conferences, and fields of study.</p></li>
|
||
<li><p><a
|
||
href="http://sonyis.me/paperpdf/Microsoft%20Academic%20Graph%20WWW%202015.pdf">An
|
||
Overview of Microsoft Academic Service (MAS) and Applications</a> -
|
||
<strong><em>WWW’15</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9075899176667058496&hl=en&as_sdt=0,5">All
|
||
Versios</a>]. Original paper on Microsoft Academic Graph.</p></li>
|
||
<li><p><a
|
||
href="https://blogs.lse.ac.uk/impactofsocialsciences/2021/05/27/goodbye-microsoft-academic-hello-open-research-infrastructure/">Goodbye,
|
||
Microsoft Academic – Hello, open research infrastructure?</a> -
|
||
<strong><em>LSE Impact Blog</em></strong>, 2021. An interpretation of
|
||
Microsoft’s strategy on research infrastructure.</p></li>
|
||
<li><p><a href="https://www.semanticscholar.org/">Semantic Scholar</a> -
|
||
<strong><em>Allen Institute for AI Research</em></strong>. AI-powered
|
||
scientific literature research tool.</p></li>
|
||
<li><p><a href="https://aclanthology.org/N18-3011/">Construction of the
|
||
Literature Graph in Semantic Scholar</a> -
|
||
<strong><em>NAACL’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5500969515339734950&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Semantic Scholar with extracting feature and metadata
|
||
from raw paper data.</p></li>
|
||
<li><p><a href="https://aclanthology.org/2020.acl-main.447/">S2ORC: The
|
||
Semantic Scholar Open Research Corpus</a> -
|
||
<strong><em>ACL’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11978464475399626925&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An open corpus of academic papers released by Semantic
|
||
Scholar.</p></li>
|
||
<li><p><a href="https://www.litmaps.com/">Litmaps</a> -
|
||
<strong><em>Litmap Ltd</em></strong>. For interactive literature map
|
||
construction and linked document management.</p></li>
|
||
<li><p><a href="https://www.vosviewer.com/">VOSviewer</a> -
|
||
<strong><em>Leiden University</em></strong>. For constructing and
|
||
visualizing bibliometric networks.</p></li>
|
||
<li><p><a href="https://www.stateoftheart.ai/">StateOfTheArt.AI</a> -
|
||
<strong><em>StateOfTheArtAI</em></strong>. For tracking, collecting and
|
||
visualizing the development of AI research.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="knowledge-management">Knowledge Management</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.loc.gov/aba/publications/FreeLCC/freelcc.html">Library
|
||
of Congress Classification</a> - <strong><em>Library of
|
||
Congress</em></strong>. Classification system of USA (PDF
|
||
only).</p></li>
|
||
<li><p><a href="http://cct.nlc.cn/">Chinese Library Classification</a> -
|
||
<strong><em>National Library of China</em></strong>. Classification
|
||
system of P. R. China (online user interface in Chinese). [<a
|
||
href="https://www.isko.org/cyclo/clc">English introduction at ISKO</a>].
|
||
[<a
|
||
href="https://en.wikipedia.org/wiki/Chinese_Library_Classification">Wikipedia-EN</a>].</p></li>
|
||
<li><p><a
|
||
href="https://rvk.uni-regensburg.de/regensburger-verbundklassifikation-online">DDC
|
||
at German National Library</a> - <strong><em>Deutsche National
|
||
Bibliothek</em></strong>. Deway Decimal Classification (DDC) based
|
||
classification system of Germany (online user interface). [<a
|
||
href="https://www.dnb.de/EN/Professionell/DDC-Deutsch/DDCinDNB/ddcindnb_node.html">DNB
|
||
Website</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.ndl.go.jp/jp/data/catstandards/classification_subject/ndlc.html">National
|
||
Dite Library Classification</a> - <strong><em>National Diet Library of
|
||
Japan</em></strong>. Classification system of Japan (PDF only).</p></li>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/List_of_Dewey_Decimal_classes">DDC
|
||
at OCLC (Wikipedia)</a> - <strong><em>Online Computer Library Center
|
||
(OCLC)</em></strong>. [<a href="https://www.oclc.org/en/home.html">OCLC
|
||
Website</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/dewey/versions/print/intro.pdf">Introduction
|
||
to DDC</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/full_manual.pdf">DDC
|
||
Manual</a>]. Dewey Decimal Classification (DDC) system for worldwide
|
||
library resouce construction. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/000.pdf">DDC
|
||
Class 000 (PDF only)</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/100.pdf">DDC
|
||
Class 100 (PDF only)</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/200.pdf">DDC
|
||
Class 200 (PDF only)</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/300.pdf">DDC
|
||
Class 300 (PDF only)</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/400.pdf">DDC
|
||
Class 400 (PDF only)</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/500.pdf">DDC
|
||
Class 500 (PDF only)</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/600.pdf">DDC
|
||
Class 600 (PDF only)</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/700.pdf">DDC
|
||
Class 700 (PDF only)</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/800.pdf">DDC
|
||
Class 800 (PDF only)</a>]. [<a
|
||
href="https://www.oclc.org/content/dam/oclc/webdewey/help/900.pdf">DDC
|
||
Class 900 (PDF only)</a>].</p></li>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Knowledge_organization">Knowledge
|
||
organization</a> - <strong><em>Wikipedia</em></strong>. Wikipedia on
|
||
knowledge organization methods.</p></li>
|
||
<li><p><a href="https://zettelkasten.de/">The Zettelkasten Method</a> -
|
||
<strong><em>Bielefeld University</em></strong>. Relating ideas in graphs
|
||
and multi-labels.</p></li>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Zettelkasten">Zettelkasten</a> -
|
||
<strong><em>Wikipedia</em></strong>. Wikipedia on the Zettelkasten
|
||
method.</p></li>
|
||
<li><p><a href="https://roamresearch.com/">Roam Research</a> -
|
||
<strong><em>Roam Research</em></strong>. For linked document management,
|
||
visualization, and sharing.</p></li>
|
||
<li><p><a href="https://foambubble.github.io/foam/">Foam</a> -
|
||
<strong><em>Foambubble</em></strong>. For linked document management,
|
||
visualization, and sharing, opensourced softward built on
|
||
VSCode.</p></li>
|
||
<li><p><a href="https://www.buildingasecondbrain.com/">Building a Second
|
||
Brain</a> - <strong><em>Forte Labs, LLC</em></strong>. Connecting ideas
|
||
in graphs.</p></li>
|
||
<li><p><a href="https://www.zotero.org/">Zotero</a> -
|
||
<strong><em>Digital Scholar</em></strong>. For reference management to
|
||
manage bibliographic data and research related materials.</p></li>
|
||
<li><p><a
|
||
href="https://pdfs.semanticscholar.org/88f8/fa9dfbc0c2b296758dd932b871917c5c775a.pdf%C2%A0">Niklas
|
||
Luhmann’s Card Index: Thinking Tool, Communication Partner, Publication
|
||
Machine</a> - <strong><em>Forgetting Machines: Knowledge Management
|
||
Evolution in Early Modern Europe, Brill</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1786807670077004336&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://culturemachine.net/wp-content/uploads/2019/01/373-604-1-PB.pdf">The
|
||
card index as creativity machine</a> - <strong><em>Culture
|
||
Machine</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9767873312286889264&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.researchgate.net/profile/Alberto-Cevolini/publication/328624186_Where_Does_Niklas_Luhmann%27s_Card_Index_Come_From/links/609f818e299bf147699a401d/Where-Does-Niklas-Luhmanns-Card-Index-Come-From.pdf">Where
|
||
Does Niklas Luhmann’s Card Index Come From?</a> - <strong><em>Erudition
|
||
and the Republic of Letters</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8279465066043884141&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A simplified introduction on Luhmann’s
|
||
Zettelkasten.</p></li>
|
||
<li><p><a
|
||
href="https://www.uni-bielefeld.de/fakultaeten/soziologie/forschung/luhmann-archiv/pdf/jschmidt_niklas-luhmanns-card-index_-sociologica_2018_12-1.pdf">Niklas
|
||
Luhmann’s Card Index: The Fabrication of Serendipity</a> -
|
||
<strong><em>Sociologica</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12440286698665929622&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://luhmann.surge.sh/communicating-with-slip-boxes">Communicating
|
||
with Slip Boxes</a> - 2019. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Communicating+with+slip+boxes+luhmann&btnG=">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h2 id="papers">Papers</h2>
|
||
<h3 id="abduction">Abduction</h3>
|
||
<h4 id="explanation">Explanation</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/abduction/index.html">Abduction</a>
|
||
- <strong><em>Plato Stanford</em></strong>. A computational philosophy
|
||
account on Abduction, one of the three thinking patterns besides
|
||
Induction and Deduction, being unique for its potential to introduce new
|
||
ideas into current knowledge.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/scientific-explanation/">Scientific
|
||
Explanation</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on Scientific Explanation, a canonical
|
||
application of Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/scientific-reduction/">Scientific
|
||
Reduction</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on Scientific Reduction, which comes
|
||
with no explicit boundary with Explanation.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logic-nonmonotonic/">Non-monotonic
|
||
Logic</a> - <strong><em>Plato Stanford</em></strong>. A computational
|
||
philosophy account on Non-monotonic Logic, a family of formal frameworks
|
||
devised to capture and represent defeasible inference.</p></li>
|
||
<li><p><a href="https://4lib.org/book/702071/e8ffe8">Philosophical
|
||
Writings of Peirce</a> - <strong><em>Courier Corporation</em></strong>,
|
||
1955. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3917019015464129592&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Original writings by C. S. Peirce, the establisher of
|
||
Abduction.</p></li>
|
||
<li><p><a href="https://www.jstor.org/stable/2183532">The Inference to
|
||
the Best Explanation</a> - <strong><em>Philosophical
|
||
Review</em></strong>, 1965. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1416627814151433560&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Lipton’s original paper on Inference to the Best
|
||
Explanation as a special case of Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://hk1lib.org/book/3594789/f39e15?id=3594789&secret=f39e15">Inference
|
||
to the Best Explanation</a> - <strong><em>Routledge</em></strong>, 1991.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=6494546505729177895&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Lipton’s book on Inference to the Best Explanation as a
|
||
special case of Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://www.taylorfrancis.com/books/mono/10.4324/9781315083223/study-thinking-jerome-bruner-jacqueline-goodnow-george-austin">A
|
||
Study of Thinking</a> - <strong><em>Routledge</em></strong>, 1956. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17466297915128086930&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A classic book on thinking patterns.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/book/10.1007/978-94-017-1733-5">Abductive
|
||
Reasoning and Learning</a> - <strong><em>Springer</em></strong>, 2000.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An introductory account on abductive reasoning.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/book/10.1007%2F1-4020-3907-7#authorsandaffiliationsbook">Abductive
|
||
Reasoning: Logical Investigations into Discovery and Explanation</a> -
|
||
<strong><em>Springer</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Abductive+Reasoning%3A+Logical+Investigations+into+Discovery+and+Explanation&btnG=">All
|
||
Versions</a>]. An introductory account on abductive reasoning.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/book/10.1007%2F978-3-642-03631-6">Abductive
|
||
Cognition: The Epistemological and Eco-Cognitive Dimensions of
|
||
Hypothetical Reasoning</a> - <strong><em>Springer</em></strong>, 2009.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=8707351442527595188&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://cognition.princeton.edu/sites/default/files/cognition/files/explanation_abductive_inference.pdf">Explanation
|
||
and Abductive Inference</a> - <strong><em>The Oxford Handbook of
|
||
Thinking and Reasoning</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16126850654692681562&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A handbook on the formulations of Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.jhu.edu/~ayuille/JHUcourses/ProbabilisticModelsOfVisualCognition2020/Lec7/chater2006probabilistica.pdf">Probabilistic
|
||
models of cognition: Conceptual foundations</a> - <strong><em>Trends in
|
||
Cognitive Sciences</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=12857321660837478492">All
|
||
Versions</a>]. A Bayesian account of Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://cognition.princeton.edu/sites/default/files/cognition/files/tics_explanation.pdf">The
|
||
structure and function of explanations</a> - <strong><em>Trends in
|
||
Cognitive Sciences</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2849189270394400667&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Basic computation modes of Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://scholar.princeton.edu/sites/default/files/cognition/files/explanatory_prefs_tics.pdf">Explanatory
|
||
Preferences Shape Learning and Inference</a> - <strong><em>Trends in
|
||
Cognitive Sciences</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2040551538203889465&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An account showing that inductive bias is critical for
|
||
explanation.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0010027715000955">The
|
||
Role of Explanatory Considerations in Updating</a> -
|
||
<strong><em>Cognition</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3089358487428261042&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.tandfonline.com/doi/full/10.1080/20445911.2016.1230122">Explanation,
|
||
updating, and accuracy</a> - <strong><em>Journal of Cognitive
|
||
Psychology</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=967127146748155733&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/2018-03972-001">Best,
|
||
second-best, and good-enough explanations: How they matter to
|
||
reasoning</a> - <strong><em>Journal of Experimental
|
||
Psychology</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?start=0&hl=en&as_sdt=0,5&cluster=3067550385175104201">All
|
||
Versions</a>]. A subjective probability account of Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661321001790">How
|
||
explanation guides belief change</a> - <strong><em>Trends in Cognitive
|
||
Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15240531165875981526&hl=en&as_sdt=2005&sciodt=2005">All
|
||
Versions</a>]. A review on the subjective probability account of
|
||
Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog2506_2">Use
|
||
of current explanations in multicausal abductive reasoning</a> -
|
||
<strong><em>Cognitive Science</em></strong>, 2001. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7816050625957759346&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/110/42/16766.short">Kinematic mental
|
||
simulations in abduction and deduction</a> - <strong><em>Proceedings of
|
||
the National Academy of Sciences</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11864820390497230588&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s11229-007-9223-4">Patterns
|
||
of abduction</a> - <strong><em>Synthese</em></strong>, 2007. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A categorization for Abduction in the account of pure
|
||
philosophy.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1570868314000895">Abduction:
|
||
A categorical characterization</a> - <strong><em>Journal of Applied
|
||
Logic</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17834260152484836885&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.journals.uchicago.edu/doi/abs/10.1086/392744">Defending
|
||
Abduction</a> - <strong><em>Philosophy of Science</em></strong>, 1999.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=13895790050138832555&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s11229-009-9709-3">On
|
||
the distinction between Peirce’s abduction and Lipton’s Inference to the
|
||
best explanation</a> - <strong><em>Synthese</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s11229-019-02337-z">Abduction − the
|
||
context of discovery + underdetermination = inference to the best
|
||
explanation</a> - <strong><em>Synthese</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007%2F3-540-45004-1_14">Towards
|
||
an Architecture for Cognitive Vision Using Qualitative Spatio-temporal
|
||
Representations and Abduction</a> - <strong><em>Spatial
|
||
Cognition</em></strong>, 2002. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8072265283930278310&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s11229-018-1824-6">Abductive
|
||
inference within a pragmatic framework</a> -
|
||
<strong><em>Synthese</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s00354-019-00059-x">Disjunctive
|
||
Abduction</a> - <strong><em>New Generation Computing</em></strong>,
|
||
2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.frontiersin.org/articles/10.3389/fpsyg.2015.00459/full">Probabilistic
|
||
alternatives to Bayesianism: the case of explanationism</a> -
|
||
<strong><em>Frontiers in Psychology</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9016714668469830914&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A non-Bayesian account of Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://www.scitepress.org/Link.aspx?doi=10.5220/0010195405620571">A
|
||
Probabilistic Theory of Abductive Reasoning</a> -
|
||
<strong><em>ICAART</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=450937566244876051&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A probabilistic perspective for interpreting Abductive
|
||
Reasoning.</p></li>
|
||
<li><p><a
|
||
href="https://www.tandfonline.com/doi/full/10.1080/09528130600558141?scroll=top&needAccess=true">The
|
||
order effect in human abductive reasoning: an empirical and
|
||
computational study</a> - <strong><em>Journal of Experimental &
|
||
Theoretical Artificial Intelligence</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3803536062463585043&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007%2F978-3-642-15223-8_5">Abduction,
|
||
Induction, and Analogy</a> - <strong><em>Model-Based Reasoning in
|
||
Science and Technology</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14979764682921693390&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The distinctions and relations between Abduction,
|
||
Induction, and Analogy.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/S0010027718301094">Remembrance
|
||
of inferences past: Amortization in human hypothesis generation</a> -
|
||
<strong><em>Cognition</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=190340622765037472&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. A rational account of human hypothesis
|
||
generation.</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/full/10.1111/j.1551-6709.2010.01142.x">The
|
||
AHA! Experience: Creativity Through Emergent Binding in Neural
|
||
Networks</a> - <strong><em>Cognitive Science</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10006889101167052798&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S2352154620300851">Explanation-seeking
|
||
curiosity in childhood</a> - <strong><em>Current Opinion in Behavioral
|
||
Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4167956555501133663&hl=en&as_sdt=2005">All
|
||
Versions</a>]. A piece of developmental pshchological evidence for
|
||
Abduction in young children.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="scientific-discovery">Scientific Discovery</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/scientific-discovery/">Scientific
|
||
Discovery</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on Scientific Discovery, the process or
|
||
product of successful scientific inquiry, sometimes an Abduction-like
|
||
(Explanation) thinking pattern.</p></li>
|
||
<li><p><a
|
||
href="https://hk1lib.org/book/2241843/c5d7b3?id=2241843&secret=c5d7b3">Models
|
||
of Discovery: And Other Topics in the Methods of Science</a> -
|
||
<strong><em>Springer</em></strong>, 1977. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9932701864897299105&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original book on search as scientific
|
||
thinking.</p></li>
|
||
<li><p><a
|
||
href="https://hk1lib.org/book/970300/6b0ff7?id=970300&secret=6b0ff7">Scientific
|
||
discovery: Computational explorations of the creative processes</a> -
|
||
<strong><em>MIT Press</em></strong>, 1987. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11327000316248254911&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A computational account unifying Scientific Discovery
|
||
with the creativity feature of Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://hk1lib.org/book/701605/02b32a?id=701605&secret=02b32a">Induction:
|
||
Processes of Inference, Learning, and Discovery</a> - <strong><em>MIT
|
||
Press</em></strong>, 1989. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12402938838725132707&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An Induction account of Scientific Discovery.</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/2000-03968-000">Exploring
|
||
science: The cognition and development of discovery processes</a> -
|
||
<strong><em>MIT Press</em></strong>, 2000. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13091264356550286420&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog1201_1">Dual
|
||
Space Search During Scientific Reasoning</a> - <strong><em>Cognitive
|
||
Science</em></strong>, 1988. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17542852673494089523&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. The original paper on the dual space search as scientific
|
||
thinking theory.</p></li>
|
||
<li><p><a
|
||
href="https://www.cmu.edu/dietrich/psychology/pdf/klahr/PDFs/schunn-klahr.pdf">Complexity
|
||
Management in a Discovery Task</a> -
|
||
<strong><em>CogSci’92</em></strong>, 1992. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18138712608977258974&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Advanced experiments on dual space search.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1071581996900324">A
|
||
dual-space model of iteratively deepening exploratory learning</a> -
|
||
<strong><em>International Journal of Human-Computer
|
||
Studies</em></strong>, 1996. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17337189265334825678&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Iterative version (in depth and in width) of dual space
|
||
search.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/S0010028583710030">Heuristics
|
||
for Scientific Experimentation: A Developmental Study</a> -
|
||
<strong><em>Cognitive Psychology</em></strong>, 1993. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2469515962071844494&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. A piece of evidence on children have basic scientific
|
||
thinking skills.</p></li>
|
||
<li><p><a
|
||
href="https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.645.248&rep=rep1&type=pdf">A
|
||
4-Space Model of Scientific Discovery</a> -
|
||
<strong><em>CogSci’95</em></strong>, 1995. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1063157789682040473&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Extending the dual space search.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.3758/BF03201090">When to
|
||
trust the data: Further investigations of system error in a scientific
|
||
reasoning task</a> - <strong><em>Memory & Cognition</em></strong>,
|
||
1996. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. A behavioral account on the shift between bottom-up
|
||
observation and top-down reasoning.</p></li>
|
||
<li><p><a
|
||
href="https://psycnet.apa.org/record/1987-20689-001">Confirmation,
|
||
disconfirmation, and information in hypothesis testing</a> -
|
||
<strong><em>Psychological Review</em></strong>, 1987. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1954141597807453515&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A psychological account on hypothesis testing.</p></li>
|
||
<li><p><a
|
||
href="https://psycnet.apa.org/record/2010-22980-001">Hypothesis
|
||
generation, sparse categories, and the positive test strategy</a> -
|
||
<strong><em>Psychological Review</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4329636480235863472&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/1990-03504-001">Children
|
||
and adults as intuitive scientists</a> - <strong><em>Psychological
|
||
Review</em></strong>, 1989. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9577945454476127070&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. A perspective against search as scientific
|
||
thinking.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/content/pdf/10.1007/s11229-019-02127-7.pdf">Abduction
|
||
and styles of scientific thinking</a> -
|
||
<strong><em>Synthese</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9336871656706514469&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A computational philosophy account connecting Abduction
|
||
and scientific thinking.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="rationalization">Rationalization</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/S0885201414000744">Imagination
|
||
and the generation of new ideas</a> - <strong><em>Cognitive
|
||
Development</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16920774374067505248&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. A piece of evidence for rationalization in
|
||
childhood.</p></li>
|
||
<li><p><a
|
||
href="http://web.mit.edu/maxs/www/papers/cogsci_2016_vapors.pdf">Coalescing
|
||
the Vapors of Human Experience into a Viable and Meaningful
|
||
Comprehension</a> - <strong><em>CogSci’16</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5460385008324352958&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Constrainted thinking as rationalization.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661319302311">How
|
||
We Know What Not To Think</a> - <strong><em>Trends in Cognitive
|
||
Sciences</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13106919756521743226&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. A comprehensive review on rationalization.</p></li>
|
||
<li><p><a
|
||
href="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/rationalization-is-rational/2A13B99ED09BD802C0924D3681FEC55B">Rationalization
|
||
is rational</a> - <strong><em>Behavioral and Brain
|
||
Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5165464589274056844&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. A rationality account on rationalization.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661321001480">Rationalizing
|
||
constraints on the capacity for cognitive control</a> -
|
||
<strong><em>Trends in Cognitive Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13065113821339619145&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/why-imaginary-worlds/CA2AB4B1E1EDD8FE965E6DDB4A047B35">Why
|
||
Imaginary Worlds? The psychological foundations and cultural evolution
|
||
of fictions with imaginary worlds</a> - <strong><em>Behavioral and Brain
|
||
Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11677245106477509648&hl=en&as_sdt=2005&sciodt=2005">All
|
||
Versions</a>]. A review of rationalization as imaginary worlds in
|
||
fictions.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="applications-in-ai">Applications in AI</h4>
|
||
<ul>
|
||
<li><p><a href="https://www.nature.com/articles/nature02236">Functional
|
||
genomic hypothesis generation and experimentation by a robot
|
||
scientist</a> - <strong><em>Nature</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A canonical application of logical abduction on
|
||
biodesign.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-021-03819-2">Highly
|
||
accurate protein structure prediction with AlphaFold</a> -
|
||
<strong><em>Nature</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A canonical application of observation- and explanation-
|
||
based method for protein structure prediction instead of
|
||
first-principle-based methods.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/0004370293900154?via%3Dihub">Interpretation
|
||
as abduction</a> - <strong><em>Artificial Intelligence</em></strong>,
|
||
1993. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12658433318211361322&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A computational account on interpretation as
|
||
Abduction.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/000437029390061F?via%3Dihub">Probabilistic
|
||
Horn abduction and Bayesian networks</a> - <strong><em>Artificial
|
||
Intelligence</em></strong>, 1993. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7728248035489349629&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Casual abduction in Bayesian networks.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007/978-3-540-39879-0_6">Abductive
|
||
Inference in Bayesian Networks: A Review</a> - <strong><em>Advances in
|
||
Bayesian Networks</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://academic.oup.com/logcom/article-abstract/2/6/719/942121?redirectedFrom=fulltext">Abductive
|
||
Logic Programming</a> - <strong><em>Journal of Logic
|
||
Computation</em></strong>, 1992. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18119357517656745518&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper in ALP.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0743106699000758">ACLP:
|
||
Abductive Constraint Logic Programming</a> - <strong><em>The Journal of
|
||
Logic Programming</em></strong>, 1999. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14319574550421192429&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper in ACLP.</p></li>
|
||
<li><p><a
|
||
href="https://web.stanford.edu/class/cs227/Readings/Abudction%20in%20LP.pdf">Abduction
|
||
in Logic Programming</a> - <strong><em>Computational
|
||
Logic</em></strong>, 2002. [<a
|
||
href="https://scholar.google.com/scholar?cluster=902643678163312237&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The revised version of the ALP paper.</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.utexas.edu/~ml/papers/raghavan.starai10.pdf">Bayesian
|
||
Abductive Logic Programs: A Probabilistic Logic for Abductive
|
||
Reasoning</a> - <strong><em>IJCAI’11</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4453424083730209198&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.utexas.edu/~ml/papers/raghavan.ecml11.pdf">Abductive
|
||
Plan Recognition by Extending Bayesian Logic Programs</a> -
|
||
<strong><em>ECML’11</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7276511797197017483&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.aaai.org/ocs/index.php/IJCAI/IJCAI13/paper/view/6624/6619">An
|
||
Approach to Abductive Reasoning in Equational Logic</a> -
|
||
<strong><em>IJCAI’13</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=686895264429811190&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ojs.aaai.org//index.php/AAAI/article/view/3964">Abduction-Based
|
||
Explanations for Machine Learning Models</a> -
|
||
<strong><em>AAAI’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7355960657107994022&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2105.10118.pdf">Probabilistic
|
||
Sufficient Explanations</a> - <strong><em>IJCAI’21</em></strong>, 2021.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=1874102360688341104&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.aclweb.org/anthology/H91-1024.pdf">Machine
|
||
Translation Using Abductive Inference</a> -
|
||
<strong><em>COLING</em></strong>, 1990. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15275163177548183539&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An application of abduction in language
|
||
translating.</p></li>
|
||
<li><p><a href="https://dspace.mit.edu/handle/1721.1/129250">Anomaly
|
||
detection through explanations</a> - <strong><em>Ph.D Dissertation
|
||
MIT</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Anomaly+detection+through+explanations&btnG=">All
|
||
Versions</a>]. An application of abduction in anomaly
|
||
detection.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2105.07758">Automated Biodesign
|
||
Engineering by Abductive Meta-Interpretive Learning</a> -
|
||
<strong><em>AAAI Spring Symposium Series 2021 on Artificial Intelligence
|
||
for Synthetic Biology</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=543730388062329581&as_sdt=0,5">All
|
||
Versions</a>]. This work proposes an automated biodesign engineering
|
||
framework empowered by Abductive Meta-Interpretive Learning (MetaAbd), a
|
||
novel machine learning approach that combines symbolic and sub-symbolic
|
||
machine learning, to further enhance the design-build-test-learn cycle
|
||
by enabling the learning machine to 1) exploit domain knowledge and
|
||
learn human-interpretable models that are expressed by formal languages
|
||
such as first-order logic; 2) simultaneously optimise the structure and
|
||
parameters of the models to make accurate numerical predictions; 3)
|
||
reduce the cost of experiments and effort on data annotation by actively
|
||
generating hypotheses and examples.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2308.12740">Human Comprehensible
|
||
Active Learning of Genome-Scale Metabolic Networks</a> -
|
||
<strong><em>AAAI Spring Symposium Series 2023 on Computational
|
||
Scientific Discovery</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&cluster=10875437066608527790">All
|
||
Versions</a>]. [<a
|
||
href="http://cogsys.org/symposium/discovery-2023/abstracts/Abstract_3169.pdf">Extended
|
||
Abstract</a>]. [<a
|
||
href="http://cogsys.org/symposium/discovery-2023/talks/Ai.pdf">Slides</a>].
|
||
This work introduces a novel machine learning framework ILP-iML1515
|
||
based on Inductive Logic Programming (ILP) that performs abductive
|
||
logical reasoning and actively learns from training examples. The
|
||
ILP-iML1515 framework 1) allows high-throughput simulations and 2)
|
||
actively selects experiments that reduce the experimental cost of
|
||
learning gene functions in comparison to randomly selected
|
||
experiments.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="bayesian-modeling">Bayesian Modeling</h3>
|
||
<h4 id="bayesian-induction">Bayesian Induction</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/epistemology-bayesian/">Bayesian
|
||
Epistemology</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on the nature of uncertainty modeling
|
||
in Bayesian Epistemology.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/nature14541.pdf">Probabilistic
|
||
machine learning and artificial intelligence</a> -
|
||
<strong><em>Nature</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1783282361269717744&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Zoubin Ghahramani’s review on Bayesian machine
|
||
learning.</p></li>
|
||
<li><p><a
|
||
href="http://web.mit.edu/cocosci/archive/Papers/tenenbaum_griffiths01.pdf">Generalization,
|
||
similarity, and Bayesian inference</a> - <strong><em>Behavioral and
|
||
Brain Sciences</em></strong>, 2001. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14074987155133342565&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Josh Tenenbaum’s review on Bayesian
|
||
generalization.</p></li>
|
||
<li><p><a
|
||
href="http://web.mit.edu/cocosci/archive/Papers/bayes.pdf">Bayesian
|
||
modeling of human concept learning</a> -
|
||
<strong><em>NeurIPS’98</em></strong>, 1998. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12185543141957001794&hl=en&as_sdt=0,5&as_vis=1">All
|
||
Versions</a>]. Original paper on Bayesian generalization.</p></li>
|
||
<li><p><a
|
||
href="http://web.mit.edu/cocosci/archive/Papers/nips99preprint.pdf">Rules
|
||
and Similarity in Concept Learning</a> -
|
||
<strong><em>NeurIPS’99</em></strong>, 1999. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10968021160883668417&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Unifying rule-based and similarity-based generalization
|
||
via Bayesian generalization.</p></li>
|
||
<li><p><a
|
||
href="http://www.charleskemp.com/papers/TenenbaumGK06.pdf">Theory-based
|
||
Bayesian models of inductive learning and reasoning</a> -
|
||
<strong><em>Trends in Cognitive Sciences</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6741344960992898446&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Josh Tenenbaum’s review on Bayesian theory
|
||
induction.</p></li>
|
||
<li><p><a
|
||
href="https://tallinzen.net/media/readings/xu_tenenbaum_2007.pdf">Word
|
||
learning as Bayesian inference</a> - <strong><em>Psychological
|
||
Review</em></strong>, 2007. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=5476233692839102256">All
|
||
Versions</a>]. [<a
|
||
href="https://psycnet.apa.org/doiLanding?doi=10.1037%2F0033-295X.114.2.245">APA</a>].
|
||
Fei Xu’s review on Bayesian word learning.</p></li>
|
||
<li><p><a
|
||
href="https://cocosci.princeton.edu/tom/papers/growamind.pdf">How to
|
||
Grow a Mind: Statistics, Structure, and Abstraction</a> -
|
||
<strong><em>Science</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2667398573353002097&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Josh Tenenbaum’s review on Bayesian theory
|
||
induction.</p></li>
|
||
<li><p><a
|
||
href="https://ai6034.mit.edu/wiki/images/LakeDec2015.pdf">Human-level
|
||
concept learning through probabilistic program induction.</a> -
|
||
<strong><em>Science</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11844685101409624506&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://cims.nyu.edu/~brenden/LakeEtAl2015Science_supp.pdf">Supplementary
|
||
Material</a>]. Bayesian program induction for few-shot
|
||
learning.</p></li>
|
||
<li><p><a
|
||
href="https://leylaroksancaglar.github.io/Caglar_Hanson_2017.pdf">Building
|
||
Machines That Learn and Think Like People</a> - <strong><em>Behavioral
|
||
and Brain Sciences</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8504723689348856287&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Brenden Lake and Josh Tenenbaum’s review on Bayesian
|
||
modeling.</p></li>
|
||
<li><p><a
|
||
href="http://web.mit.edu/cocosci/archive/Papers/cogsci01_final.pdf">The
|
||
rational basis of representativeness</a> -
|
||
<strong><em>CogSci’01</em></strong>, 2001. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11464039134248091466&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2011/hash/2c89109d42178de8a367c0228f169bf8-Abstract.html">Testing
|
||
a Bayesian Measure of Representativeness Using a Large Image
|
||
Database</a> - <strong><em>NeurIPS’11</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8576570792794301292&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://cocosci.princeton.edu/tom/papers/abbott_cogsci2012_wordnet.pdf">Constructing
|
||
a hypothesis space from the Web for large-scale Bayesian word
|
||
learning</a> - <strong><em>CogSci’12</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9266416266046851766&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://escholarship.org/content/qt1md755ng/qt1md755ng.pdf">Modeling
|
||
rules and similarity in colexification</a> -
|
||
<strong><em>CogSci’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=11578380234126546169">All
|
||
Versions</a>]. Rule- and similarity-based generalization in
|
||
colexification.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="generative-model">Generative Model</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://github.com/YuzheSHI/generative-modeling-explained">Generative
|
||
Modeling Explained</a> - <strong><em>Statistical Machine Learning
|
||
Tutorials</em></strong>, 2022. This tutorial on generative modeling is
|
||
in part of Statistical Machine Learning Tutorial by Ying Nian Wu at UCLA
|
||
Statistics. The tutorial goes over the key equations and algorithms for
|
||
learning recent generative models, including energy-based models,
|
||
diffusion/score-based models, autoregressive/flow-based models, VAEs,
|
||
and GANs, and explains the connections between these models.</p></li>
|
||
<li><p><a
|
||
href="https://www.taylorfrancis.com/books/mono/10.1201/9780429258411/bayesian-data-analysis-andrew-gelman-donald-rubin-john-carlin-hal-stern">Bayesian
|
||
Data Analysis</a> - <strong><em>Chapman and Hall/CRC</em></strong>,
|
||
1995. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5067275302121330689&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Don Rubin’s introductory book on Bayesian
|
||
models.</p></li>
|
||
<li><p><a
|
||
href="https://dash.harvard.edu/bitstream/handle/1/3637117/Mumford_FRAME.pdf?sequence=1">Filters,
|
||
random fields and maximum entropy (FRAME): Towards a unified theory for
|
||
texture modeling</a> - <strong><em>International Journal of Computer
|
||
Vision</em></strong>, 1998. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11604954524863138240&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Song-Chun Zhu’s original paper on energy-based generative
|
||
texture modeling.</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.jhu.edu/~ayuille/pubs/ucla/A189_dkersten_ARP2004.pdf">Object
|
||
Perception as Bayesian Inference</a> - <strong><em>Annual Review of
|
||
Psychology</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=1611451804975333652">All
|
||
Versions</a>]. Alan Yuille’s review on Bayesian object
|
||
perception.</p></li>
|
||
<li><p><a href="http://www.stat.ucla.edu/~ywu/QAM2018.pdf">A tale of
|
||
three probabilistic families: Discriminative, descriptive, and
|
||
generative models</a> - <strong><em>Quarterly of Applied
|
||
Mathematics</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=6129609629126793774">All
|
||
Versions</a>]. Ying Nian Wu’s review on three families of statistical
|
||
modeling.</p></li>
|
||
<li><p><a
|
||
href="http://www.stat.ucla.edu/~sczhu/papers/Quarterly_final.pdf">From
|
||
information scaling of natural images to regimes of statistical
|
||
models</a> - <strong><em>Quarterly of Applied Mathematics</em></strong>,
|
||
2008. [<a
|
||
href="https://scholar.google.com/scholar?start=0&hl=en&as_sdt=0,5&cluster=17387130978932998303">All
|
||
Versions</a>]. A statistical account for the shift from textons to
|
||
texture.</p></li>
|
||
<li><p><a href="https://proceedings.mlr.press/v48/xiec16.html">A Theory
|
||
of Generative ConvNet</a> - <strong><em>ICML’16</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11062907630625111054&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/ielaam/34/8922815/8519332-aam.pdf">Cooperative
|
||
Training of Descriptor and Generator Networks</a> - <strong><em>IEEE
|
||
Transactions on Pattern Analysis and Machine Intelligence</em></strong>,
|
||
2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18202808849093155435&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2020/hash/fa3060edb66e6ff4507886f9912e1ab9-Abstract.html">Learning
|
||
Latent Space Energy-Based Prior Model</a> -
|
||
<strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=9945264852135249894">All
|
||
Versions</a>]. [<a
|
||
href="https://bpucla.github.io/latent-space-ebm-prior-project/">Project</a>].
|
||
[<a href="https://github.com/bpucla/latent-space-EBM-prior">Code</a>]. A
|
||
milestone paper on Latent Energy-Based Model.</p></li>
|
||
<li><p><a href="https://openreview.net/forum?id=v_1Soh8QUNc">Learning
|
||
Energy-Based Models by Diffusion Recovery Likelihood</a> -
|
||
<strong><em>ICLR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=4399294843209736764">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/ruiqigao/recovery_likelihood">Code</a>].</p></li>
|
||
<li><p><a
|
||
href="https://openreview.net/forum?id=PxTIG12RRHS&utm_campaign=NLP%20News&utm_medium=email&utm_source=Revue%20newsletter">Score-Based
|
||
Generative Modeling through Stochastic Differential Equations</a> -
|
||
<strong><em>ICLR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14592788616550656262">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="http://proceedings.mlr.press/v119/li20i.html">Latent
|
||
Space Factorisation and Manipulation via Matrix Subspace Projection</a>
|
||
- <strong><em>ICML’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9592355331559392684&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.dam.brown.edu/people/mumford/vision/papers/1997e--MinimaxEntropy-NC.pdf">Minimax
|
||
entropy principle and its application to texture modeling</a> -
|
||
<strong><em>Neural Computing</em></strong>, 1997. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=407872717119429940">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://www.stat.ucla.edu/~ywu/research/papers/PXDA.pdf">Parameter
|
||
Expansion for Data Augmentation</a> - <strong><em>Journal of the
|
||
American Statistical Association</em></strong>, 1999. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=15342818142955984734">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://www.stat.ucla.edu/~sczhu/papers/DDMCMC_reprint.pdf">Image
|
||
segmentation by data-driven markov chain monte carlo</a> -
|
||
<strong><em>IEEE Transactions on Pattern Analysis and Machine
|
||
Intelligence</em></strong>, 2002. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3461400072144667491&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Classic method for image segmentation via generative
|
||
modeling.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2006/file/87f4d79e36d68c3031ccf6c55e9bbd39-Paper.pdf">Efficient
|
||
Learning of Sparse Representations with an Energy-Based Model</a> -
|
||
<strong><em>NeurIPS’06</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2247668190782691760&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="http://yann.lecun.com/exdb/publis/orig/lecun-06.pdf">A
|
||
Tutorial on Energy-Based Learning</a> - <strong><em>Predicting
|
||
Structured Data, MIT Press</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8819502341081664768&hl=en&as_sdt=0,5">All
|
||
Versiosn</a>]. Yann LeCun’s tutorial on energy-based learning.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1511.06434">Unsupervised
|
||
Representaton Learning with Deep Convolutional Generative Adversarial
|
||
Networks</a> - <strong><em>ICLR’16</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3321343160055675528&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.jmlr.org/papers/volume20/18-173/18-173.pdf">Analysis
|
||
of Langevin Monte Carlo via Convex Optimization</a> -
|
||
<strong><em>Journal of Machine Learning Research</em></strong>, 2019.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=5305860199396047317&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.jhu.edu/~ayuille/JHUcourses/ProbabilisticModelsOfVisualCognition2020/Lec22/GeorgeCAPCHAS.pdf">A
|
||
generative vision model that trains with high data efficiency and breaks
|
||
text-based CAPTCHAs</a> - <strong><em>Science</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1478382321633671444&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://gershmanlab.com/pubs/Dasgupta17.pdf">Where do
|
||
hypotheses come from?</a> - <strong><em>Cognitive
|
||
Psychology</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17480320046655923235&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. A Bayesian account for modeling basic rules as the
|
||
hypothesis space.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="nonparametric-model">Nonparametric Model</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://people.stat.sc.edu/hansont/stat740/Ferguson1973.pdf">A
|
||
Bayesian Analysis of Some Non-parametric Problems</a> - <strong><em>The
|
||
Annals of Statistics</em></strong>, 1973. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3969163427460060902&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A classic review on non-parametric problems.</p></li>
|
||
<li><p><a
|
||
href="https://people.eecs.berkeley.edu/~jordan/courses/281B-spring04/readings/antoniak.pdf">Mixtures
|
||
of Dirichlet Process with Applications to Bayesian Nonparametric
|
||
Problems</a> - <strong><em>The Annals of Statistics</em></strong>, 1974.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=17937202534282344046&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on Dirichlet Process modeling for
|
||
non-parametric problems.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0022000000917112">Latent
|
||
Semantic Indexing: A Probabilistic Analysis</a> - <strong><em>Journal of
|
||
Computer and System Sciences</em></strong>, 2000. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7296120469860429813&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on hierarchical topic model.</p></li>
|
||
<li><p><a
|
||
href="https://projecteuclid.org/journals/statistical-science/volume-19/issue-1/Nonparametric-Bayesian-Data-Analysis/10.1214/088342304000000017.full">Nonparametric
|
||
Bayesian Data Analysis</a> - <strong><em>Statistical
|
||
Science</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13476170780072319995&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/doi/abs/10.1073/pnas.0307752101">Finding
|
||
scientific topics</a> - <strong><em>Proceedings of the National Academy
|
||
of Sciences</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17382767110929995134">All
|
||
Versions</a>]. Application on scientific paper ananlysis for
|
||
hierarchical topic model.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2003/file/7b41bfa5085806dfa24b8c9de0ce567f-Paper.pdf">Hierarchical
|
||
topic models and the nested Chinese restaurant process</a> -
|
||
<strong><em>NeurIPS’03</em></strong>, 2003. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15040818675282958700&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper for nested Chinese restaurant
|
||
process.</p></li>
|
||
<li><p><a
|
||
href="https://www.aaai.org/Papers/AAAI/2006/AAAI06-061.pdf">Learning
|
||
Systems of Concepts with an Infinite Relational Model</a> -
|
||
<strong><em>AAAI’06</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3207350432755252565&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/abs/10.1145/1667053.1667056">The
|
||
nested chinese restaurant process and bayesian nonparametric inference
|
||
of topic hierarchies</a> - <strong><em>Journal of the ACM</em></strong>,
|
||
2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8216933258869737505&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://mlg.eng.cam.ac.uk/zoubin/papers/ibptr.pdf">Infinite Latent
|
||
Feature Models and the Indian Buffet Process</a> - <strong><em>Gatsby
|
||
Computational Neuroscience Unit Technical Report 2005-001</em></strong>,
|
||
2005. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13180738480564152907&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://jmlr.org/papers/v12/griffiths11a.html">The
|
||
Indian Buffet Process: An Introduction and Review</a> -
|
||
<strong><em>Journal of Machine Learning Research</em></strong>, 2011.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=6301314251995890943&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Tom Griffiths and Zoubin Ghahramani’s review on infinite
|
||
models, including the Chinese Restaurant Process (CRP) and the Indian
|
||
Buffet Process (IBP).</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.ubc.ca/~nando/papers/npblog.pdf">Nonparametric
|
||
Bayesian Logic</a> - <strong><em>UAI’05</em></strong>, 2005. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18267211625980322095&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The first paper integrating logic into non-parametric
|
||
model.</p></li>
|
||
<li><p><a
|
||
href="https://www.dbs.ifi.lmu.de/~yu_k/uai06_relation.pdf">Infinite
|
||
Hidden Relational Models</a> - <strong><em>UAI’06</em></strong>, 2006.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=2143172296528388141&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://alchemy.cs.washington.edu/papers/kok07/kok07.pdf">Statistical
|
||
Predicate Invention</a> - <strong><em>ICML’07</em></strong>, 2007. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17009312281859401704&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Treating predicate invention as a non-parametric problem,
|
||
in the account of statistics.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="bayesian-optimization">Bayesian Optimization</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.cs.princeton.edu/~rpa/pubs/shahriari2016loop.pdf">Taking
|
||
the Human Out of the Loop: A Review of Bayesian Optimization</a> -
|
||
<strong><em>Proceedings of the IEEE</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2039456143890648437&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2012/file/05311655a15b75fab86956663e1819cd-Paper.pdf">Practical
|
||
Bayesian Optimization of Machine Learning Algorithms</a> -
|
||
<strong><em>NeurIPS’12</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14442949298925775705&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper for applying Bayesian optimization to
|
||
machine learning hyperparameter selection.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1807.02811">A Tutorial on Bayesian
|
||
Optimization</a> - 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7971934771645047583&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="concepts">Concepts</h3>
|
||
<h4 id="theory-of-concepts">Theory of Concepts</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/concepts/">Concepts</a> -
|
||
<strong><em>Plato Stanford</em></strong>. A collection of the
|
||
computational philosophical debates about the concepts.</p></li>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Theory-theory">Theory-theory</a> -
|
||
<strong><em>Wikipedia</em></strong>. Wikipedia for the Theory theory, a
|
||
perspective that contextualizes concepts in theoretical (or empirical)
|
||
systems.</p></li>
|
||
<li><p><a href="https://hk1lib.org/book/3659332/11fa44">Conceptual
|
||
Change in Childhood</a> - <strong><em>MIT Press</em></strong>, 1985. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=conceptual+change+in+childhood+susan+carey&btnG=">All
|
||
Versions</a>]. Susan Carey’s book on the theory theory of concepts in
|
||
child development.</p></li>
|
||
<li><p><a
|
||
href="http://library.lol/main/6A8215E9BAEB77F198C98CD75C517E02">Words,
|
||
thoughts, and theories</a> - <strong><em>MIT Press</em></strong>, 1997.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=16726462136203686735&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Alison Gopnik’s book that articulates and defends the
|
||
“theory theory” of cognitive and semantic development, the idea that
|
||
infants and young children, like scientists, learn about the world by
|
||
forming and revising theories-a view of the origins of knowledge and
|
||
meaning that has broad implications for cognitive science.</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/1994-97940-009">The
|
||
Theory Theory</a> - <strong><em>Mapping the mind: Domain specificity in
|
||
cognition and culture, Cambridge University Press</em></strong>, 1994.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=9397889700764191662&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Alison Gopnik’s original paper on the theory
|
||
theory.</p></li>
|
||
<li><p><a
|
||
href="https://hk1lib.org/book/844457/42178f?id=844457&secret=42178f">The
|
||
Origin of Concepts</a> - <strong><em>Oxford University
|
||
Press</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11493102398422813821&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Susan Carey’s extended book on the theory theory of
|
||
concepts in child development.</p></li>
|
||
<li><p><a href="https://osf.io/preprints/psyarxiv/xrnb2">What we mean
|
||
when we say semantic: A Consensus statement on the nomenclature of
|
||
semantic memory</a> - 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7464626532716945232&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The aim of this multidisciplinary workgroup was to
|
||
establish consensus definitions for some of the major recurring
|
||
constructs in semantic research (e.g., concept, amodal, abstract). These
|
||
efforts yielded a glossary consisting of succinct definitions,
|
||
agreement, subjective confidence ratings, relevant theoretical
|
||
background, and principled dissenting views. These core definitions will
|
||
potentially yield benchmarks for aligning perspectives and improving
|
||
cross-disciplinary communication in semantic research.</p></li>
|
||
<li><p><a
|
||
href="https://psycnet.apa.org/record/2012-12791-001">Reconstructing
|
||
constructivism: Causal models, Bayesian learning mechanisms, and the
|
||
theory theory</a> - <strong><em>Psychological Bulletin</em></strong>,
|
||
2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11218217347365817167&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Alison Gopnik’s review on the constructivism idea of
|
||
developmental research, including the theory theory of
|
||
concepts.</p></li>
|
||
<li><p><a
|
||
href="https://groups.psych.northwestern.edu/gentner/newpdfpapers/MedinGoldstoneGentner90.pdf">Similarity
|
||
involving attributes and relations: Judgments of similarity and
|
||
difference are not inverses</a> - <strong><em>Psychological
|
||
Science</em></strong>, 1990. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13205938250772079784&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Theory on similarity judgement by attributes and
|
||
relations.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="human-concept-representation">Human Concept Representation</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="http://behavioralhealth2000.com/wp-content/uploads/2017/01/Organizing-conceptual-knowledge-in-humans-with-a-gridlike-code.pdf">Organizing
|
||
conceptual knowledge in humans with a gridlike code</a> -
|
||
<strong><em>Science</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10995575332310321503&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Original findings suggest that global relational codes
|
||
may be used to organize nonspatial conceptual representations and that
|
||
these codes may have a hexagonal gridlike pattern when conceptual
|
||
knowledge is laid out in two continuous dimensions.</p></li>
|
||
<li><p><a
|
||
href="https://pure.mpg.de/rest/items/item_3007836/component/file_3379059/content">Navigating
|
||
cognition: Spatial codes for human thinking</a> -
|
||
<strong><em>Science</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1407237757770081862&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A framework that operates across information domains to
|
||
support a wide spectrum of cognitive functions, where place and grid
|
||
cell population codes provide a representational format to map variable
|
||
dimensions of cognitive spaces.</p></li>
|
||
<li><p><a
|
||
href="https://www.sas.upenn.edu/psych/epsteinlab/pdfs/Peer%20Brunec%20Newcombe%20Epstein%20TiCS%202020%20Cog%20maps%20and%20cog%20graphs.pdf">Structuring
|
||
Knowledge with Cognitive Maps and Cognitive Graphs</a> -
|
||
<strong><em>Trends in Cognitive Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7196012353183004425&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Russel Epstein’s review on evidence suggesting that both
|
||
map-like and graph-like representations exist in the mind/brain that
|
||
rely on partially overlapping neural systems.</p></li>
|
||
<li><p><a
|
||
href="https://www.polyu.edu.hk/cbs/rclcn/images/cdl_articles/H/Huth_et_al._2016.pdf">Natural
|
||
speech reveals the semantic maps that tile human cerebral cortex</a> -
|
||
<strong><em>Nature</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14997953800741854188&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/HuthLab/speechmodeltutorial">Code &
|
||
Tutorial</a>]. Systematically mapping semantic selectivity across the
|
||
cortex using voxel-wise modelling of functional MRI data collected while
|
||
subjects listened to hours of narrative stories, showing that the
|
||
semantic system is organized into intricate patterns that seem to be
|
||
consistent across individuals.</p></li>
|
||
<li><p><a
|
||
href="http://bilab.bnu.edu.cn/paper/2021/Wang_2021_Psychology%20Science.pdf">Idiosyncratic
|
||
Tower of Babel: Individual differences in word-meaning representation
|
||
increase as word abstractness increases</a> - <strong><em>Psychological
|
||
Science</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18214600097352809308&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Uncovering the cognitive and neural origins of
|
||
word-meaning disagreements across individuals.</p></li>
|
||
<li><p><a
|
||
href="https://cap.csail.mit.edu/sites/default/files/research-pdfs/Semantic%20projection%20recovers%20rich%20human%20knowledge%20of%20multiple%20object%20features%20from%20word%20embeddings.pdf">Semantic
|
||
projection recovers rich human knowledge of multiple object features
|
||
from word embeddings</a> - <strong><em>Nature Human
|
||
Behavior</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2499199921371106654&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Proposing a domain-general method to extract
|
||
context-dependent relationships from word embeddings: ‘semantic
|
||
projection’ of word-vectors onto lines that represent multiple
|
||
dimensions of features, which recovers human judgements across various
|
||
object categories and properties.</p></li>
|
||
<li><p><a
|
||
href="https://www.frontiersin.org/articles/10.3389/fpsyg.2014.00385/full">Using
|
||
a high-dimensional graph of semantic space to model relationships among
|
||
words</a> - <strong><em>Frontiers in Psychology</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=472523411548302295&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. First-order similarity and second-order relation metrics
|
||
for word embedding.</p></li>
|
||
<li><p><a
|
||
href="http://bilab.bnu.edu.cn/paper/2023/Tian_2023_CC.pdf">Simple shape
|
||
feature computation across modalities: convergence and divergence
|
||
between the ventral and dorsal visual streams</a> - <strong><em>Cerebral
|
||
Cortex</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5977822802446917081&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Visual and haptic shape perception fMRI experiments
|
||
suggesting that mid-level shape features are represented in a
|
||
modality-independent manner in both the ventral and dorsal
|
||
streams.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/s41597-019-0341-x">The
|
||
Database of Cross-Linguistic Colexifications, reproducible analysis of
|
||
cross-linguistic polysemies</a> - <strong><em>Scientific
|
||
Data</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a href="https://clics.clld.org/">Project</a>]. CLICS
|
||
tackles interconnected interdisciplinary research questions about the
|
||
colexifcation of words across semantic categories in the world’s
|
||
languages, and show-cases best practices for preparing data for
|
||
cross-linguistic research.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S001002772300183X">Locating
|
||
what comes to mind in empirically derived representational spaces</a> -
|
||
<strong><em>Cognition</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=57834483230365927&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An evidence-based study concluding that people call
|
||
category members to mind according to their location in representational
|
||
space, specifically based on the predicted usefulness of considering
|
||
category members with particular features.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="ai-concept-representation">AI Concept Representation</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/full/10.1126/science.ade4401">A
|
||
principal odor map unifies diverse tasks in olfactory perception</a> -
|
||
<strong><em>Science</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17847258457660438418&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/osmoai/publications/tree/main/lee_et_al_2023">Code</a>].
|
||
[<a
|
||
href="https://www.kaggle.com/datasets/aryanamitbarsainyan/multi-labelled-smiles-odors-dataset">Data
|
||
(Reproduced)</a>]. [<a
|
||
href="https://centaur.reading.ac.uk/113304/1/Mayhew%20et%20al%20for%20Centaur.pdf">Preprint</a>].
|
||
[<a href="https://www.thegoodscentscompany.com/">GoodScents
|
||
Database</a>]. [<a
|
||
href="http://www.leffingwell.com/bacispmp.htm">Leffingwell
|
||
Database</a>]. A Principal Odor Map (POM) that preserves perceptual
|
||
relationships and enables odor quality prediction for novel
|
||
odorants.</p></li>
|
||
<li><p><a href="https://elifesciences.org/articles/82502">Metabolic
|
||
activity organizes olfactory representations</a> -
|
||
<strong><em>eLife</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8857896396450033667&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/osmoai/publications/tree/main/qian_et_al_2023">Code
|
||
& Data</a>]. Odorous compounds with similar POM representations are
|
||
more likely to co-occur within a substance and be metabolically closely
|
||
related; metabolic reaction sequences also follow smooth paths in POM
|
||
despite large jumps in molecular structure.</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/abstract/document/9136877">A
|
||
Review of Tactile Information: Perception and Action Through Touch</a> -
|
||
<strong><em>IEEE Transactions on Robotics</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15493221881484741343&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://www.researchgate.net/profile/Qiang-Li-110/publication/342797645_A_Review_of_Tactile_Information_Perception_and_Action_Through_Touch/links/602f95bc92851c4ed5806e9f/A-Review-of-Tactile-Information-Perception-and-Action-Through-Touch.pdf">ResearchGate</a>].
|
||
A hierarchy consisting of raw, contact, object, and action levels to
|
||
structure the tactile information.</p></li>
|
||
<li><p><a
|
||
href="https://openaccess.thecvf.com/content/CVPR2023/html/Girdhar_ImageBind_One_Embedding_Space_To_Bind_Them_All_CVPR_2023_paper.html">ImageBind:
|
||
One Embedding Space To Bind Them All</a> -
|
||
<strong><em>CVPR’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1657173986906232916&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/facebookresearch/ImageBind">Project</a>].
|
||
Cross-modality representation fusion by aligning all other modalities to
|
||
the visual modality.</p></li>
|
||
<li><p><a href="https://escholarship.org/uc/item/44s454ng">Semantic
|
||
features of object concepts generated with GPT-3</a> -
|
||
<strong><em>CogSci’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16958563995984242923&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Testing the semantic attributes of the concepts generated
|
||
by the large language model GPT-3.</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/8953737">Connecting
|
||
Touch and Vision via Cross-Modal Prediction</a> -
|
||
<strong><em>CVPR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17326564895972374001&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/YunzhuLi/VisGel">Project</a>].</p></li>
|
||
<li><p><a href="https://aclanthology.org/2022.tacl-1.69/">Unit Testing
|
||
for Concepts in Neural Networks</a> - <strong><em>Transactions of the
|
||
Association for Computational Linguistics</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3036662275506971282&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Testing the concept representation by neural networks
|
||
through Fodor’s theory of concepts.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2402.10588">Do Llamas Work in
|
||
English? On the Latent Language of Multilingual Transformers</a> - 2024.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=5847238732288003106&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A preliminary work empirically showing that the
|
||
intermediate embeddings of multilingual Transformers (1) start far away
|
||
from output token embeddings; (2) already allow for decoding a
|
||
semantically correct next token in the middle layers, but give higher
|
||
probability to its version in English than in the input language; (3)
|
||
finally move into an input-language-specific region of the embedding
|
||
space. Also, the embedding of abstract concept space lies closer to
|
||
English than to other languages.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="complexity-information-theory">Complexity & Information
|
||
Theory</h3>
|
||
<h4 id="theory">Theory</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="http://www.cs.yale.edu/homes/yry/readings/general/shannon1948.pdf">A
|
||
Mathematical Theory of Communication</a> - <strong><em>The Bell System
|
||
Technical Journal</em></strong>, 1948. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8313213127749369813&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Shannon’s original paper on Information Theory.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/content/pdf/10.1007/978-3-030-11298-1.pdf">An
|
||
introduction to Kolmogorov complexity and its applications</a> -
|
||
<strong><em>Springer</em></strong>, 2008. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8746708322477453221&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The introductory book for Algorithmic Information Theory,
|
||
especially the Kolmogorov complexity theory.</p></li>
|
||
<li><p><a
|
||
href="https://psycnet.apa.org/record/1973-01647-001">Complexity and the
|
||
representation of patterned sequences of symbols</a> -
|
||
<strong><em>Psychological Review</em></strong>, 1972. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3426861135318645138&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Herbert Simon’s review on subjective complexity.</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/abstract/document/1057698">Visual
|
||
Pattern Discrimination</a> - <strong><em>IRE Transactions on Information
|
||
Theory</em></strong>, 1962. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10729525966103382864&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5390997">Algorithmic
|
||
Information Theory</a> - <strong><em>IBM Journal of Research and
|
||
Development</em></strong>, 1977. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14735710867906424793&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Chaitin’s original paper on Algorithmic Information
|
||
Theory.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2003/hash/b06b5541a62ed438f956b662b4e1ec28-Abstract.html">From
|
||
Algorithmic to Subjective Randomness</a> -
|
||
<strong><em>NeurIPS’03</em></strong>, 2003. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14721764738308036578">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://proceedings.mlr.press/v202/shi23i.html">On the
|
||
Complexity of Bayesian Generalization</a> -
|
||
<strong><em>ICML’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5817813824878811147&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/YuzheSHI/bayesian-generalization-complexity">Code</a>].
|
||
[<a
|
||
href="https://drive.google.com/file/d/1eCuFqBYN8kuiAmoVtXWedXW0r0TdY55W/view">Models</a>].
|
||
A concept complexity account for rule- and similarity-based Bayesian
|
||
concept generalization.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="dimensionality-reduction">Dimensionality Reduction</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1084.4695&rep=rep1&type=pdf">A
|
||
global geometric framework for nonlinear dimensionality reduction</a> -
|
||
<strong><em>Science</em></strong>, 2000. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14602426245887619907">All
|
||
Versions</a>]. The original paper on spectrum clustering.</p></li>
|
||
<li><p><a
|
||
href="https://asset-pdf.scinapse.io/prod/2100495367/2100495367.pdf">Reducing
|
||
the dimensionality of data with neural networks</a> -
|
||
<strong><em>Science</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15344645275208957628&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on Variational Autoencoder.</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1206.5538.pdf">Representation
|
||
Learning: A Review and New Perspectives</a> - <strong><em>IEEE
|
||
Transactions on Pattern Analysis and Machine Intelligence</em></strong>,
|
||
2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=559463397382443088&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Yoshua Bengio’s review on representation
|
||
learning.</p></li>
|
||
<li><p><a
|
||
href="http://www.stat.ucla.edu/~jxie/personalpage_file/publications/representation_learning_Review.pdf">Representation
|
||
Learning: A Statistical Perspective</a> - <strong><em>Annual Review of
|
||
Statistics and Its Application</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14358027809538175293&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Song-Chun Zhu and Ying Nian Wu’s review on representation
|
||
learning, in an account of statistics.</p></li>
|
||
<li><p><a
|
||
href="http://robotics.caltech.edu/wiki/images/8/8f/DeepLearningBottleneck.pdf">Deep
|
||
Learning and the Information Bottleneck Principle</a> - <strong><em>IEEE
|
||
Information Theory Workshop’15</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13152354842433826281&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The first paper identifying the problem of information
|
||
bottleneck in representation learning.</p></li>
|
||
<li><p><a
|
||
href="https://artemyk.github.io/assets/pdf/papers/Saxe%20et%20al_2019_On%20the%20information%20bottleneck%20theory%20of%20deep%20learning.pdf">On
|
||
the information bottleneck theory of deep learning</a> -
|
||
<strong><em>Journal of Statistical Mechanics: Theory and
|
||
Experiment</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12271240925674881982&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="visual-complexity">Visual Complexity</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.researchgate.net/profile/Don-Donderi-2/publication/7337589_Visual_Complexity_A_Review/links/5f0875ed45851550509a3a7a/Visual-Complexity-A-Review.pdf">Visual
|
||
complexity: a review</a> - <strong><em>Psychological
|
||
Bulletin</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10747901143387624939&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://psycnet.apa.org/record/2006-00818-005">APA</a>]. A
|
||
psychological account on visual complexity.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0141938205000120">Compressed
|
||
File Length Predicts Search Time and Errors on Visual Displays</a> -
|
||
<strong><em>Displays</em></strong>, 2005. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15600966633648834042&as_sdt=0,5">All
|
||
Versions</a>]. Compressed file size, an objective, easily obtained
|
||
measure of display complexity, predicts both subjective complexity
|
||
judgments and objective search performance. It is analogous to
|
||
algorithmic complexity, a theoretical but impractical measure of bit
|
||
string complexity. The data suggest that it may be possible to use the
|
||
compressed file size measure to predict display performance in applied
|
||
tasks.</p></li>
|
||
<li><p><a
|
||
href="https://stefan.winklerbros.net/Publications/qomex2013si.pdf">Image
|
||
complexity and spatial information</a> - <strong><em>International
|
||
Workshop on Quality of Multimedia Experience</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16011036229039693102&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://perception.jhu.edu/files/PDFs/21_Complexity_Speaking/SunFirestone_SpeakingSeeing_2021_JEPG.pdf">Seeing
|
||
and speaking: How verbal “description length” encodes visual
|
||
complexity</a> - <strong><em>Journal of Experimental
|
||
Psychology</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=246820603191585233&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://psycnet.apa.org/record/2021-83037-001">APA</a>]. Empirical
|
||
evidencs showing the relation between visual complexity and description
|
||
length.</p></li>
|
||
<li><p><a
|
||
href="https://pure.mpg.de/rest/items/item_3380375/component/file_3383568/content">How
|
||
variability shapes learning and generalization</a> - <strong><em>Trends
|
||
in Cognitive Sciences</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10940775338620708972&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review on the trade-off between
|
||
variability and generalization ability.</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2205.05666.pdf">Identifying
|
||
concept libraries from language about object structure</a> -
|
||
<strong><em>CogSci’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4019205027627496528&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0010027722003158">Show
|
||
or tell? Exploring when (and why) teaching with language outperforms
|
||
demonstration</a> - <strong><em>Cognition</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11837154580063293174&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The findings of this paper suggest that language
|
||
communicates complex concepts by directly transmitting abstract rules.
|
||
In contrast, demonstrations transmit examples, requiring the learner to
|
||
infer the rules.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="communications">Communications</h3>
|
||
<h4 id="non-verbal-communication">Non-Verbal Communication</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1551-6709.2009.01090.x">The
|
||
Interactive Evolution of Human Communication Systems</a> -
|
||
<strong><em>Cognitive Science</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6689941517686043970&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Nicolas Fay’s original paper on iconicity.</p></li>
|
||
<li><p><a href="https://benjamins.com/catalog/pc.22.2.05fay">Iconicity:
|
||
From sign to system in human communication and language</a> -
|
||
<strong><em>Pragmatics & Cognition</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8525760321117094567&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Nicolas Fay’s account on the emergence of iconic
|
||
language.</p></li>
|
||
<li><p><a
|
||
href="https://journals.sagepub.com/doi/abs/10.1177/108835769400900301">The
|
||
Picture Exchange Communication System</a> - <strong><em>Behavior
|
||
Modification</em></strong>, 1994. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18113491434570143349&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/full/10.1080/15326900701221363">Graphical
|
||
Language Games: Interactional Constraints on Representational Form</a> -
|
||
<strong><em>Cognitive Science</em></strong>, 2007. [<a
|
||
href="https://scholar.google.com/scholar?cluster=280214578402050136&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The first paper introducing the graphical language
|
||
game.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0378216614001830">A
|
||
multimodal discourse theory of visual narrative</a> -
|
||
<strong><em>Journal of Pragmatics</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=912273653379961242&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ayankumarbhunia.github.io/pixelor/image/pixelor.pdf">Pixelor:
|
||
A Competitive Sketching AI Agent. So you think you can beat me?</a> -
|
||
<strong><em>ACM SIGGRAPH’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6676723059377806081&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a href="http://sketchx.ai/pixelor">Project</a>].
|
||
Rationality in feature sketching.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s42113-019-00058-7">Pragmatic
|
||
Inference and Visual Abstraction Enable Contextual Flexibility During
|
||
Visual Communication</a> - <strong><em>Computational Brain &
|
||
Behavior</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A computational account on the rational behavior in
|
||
graphical language games.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2111.14210">Emergent Graphical
|
||
Conventions in a Visual Communication Game</a> - 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6476453985812346727&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A computational account on the emergence of iconic
|
||
language.</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/abs/10.1145/3610591.3616427">AI
|
||
Nüshu: An Exploration of Language Emergence in Sisterhood Through the
|
||
Lens of Computational Linguistics</a> - <strong><em>ACM SIGGRAPH
|
||
Asia’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6849286654402017109&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. By continually observing their environment and
|
||
communicating, AI agents trained in the Chinese dictionary and the Nüshu
|
||
corpus collaborate towards creating a standard writing system to encode
|
||
Chinese.</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/118/12/e2016569118">Communicating
|
||
artificial neural networks develop efficient color-naming systems</a> -
|
||
<strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1640459156303560508&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Simulating the emergence of code as the communication
|
||
bottleneck in color learning task.</p></li>
|
||
<li><p><a
|
||
href="https://escholarship.org/content/qt9p70d5s9/qt9p70d5s9.pdf">Bridging
|
||
cultural and cognitive perspectives on similarity reasoning</a> -
|
||
<strong><em>CogSci’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Bridging+cultural+and+cognitive+perspectives+on+similarity+reasoning&btnG=">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.eva.mpg.de/documents/Elsevier/Liszkowski_Twelve_Cognition_2008_1554509.pdf">Twelve-month-olds
|
||
communicate helpfully and appropriately for knowledgeable and ignorant
|
||
partners</a> - <strong><em>Cognition</em></strong>, 2008. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8202048572661677635&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on child pointing.</p></li>
|
||
<li><p><a
|
||
href="https://pure.mpg.de/rest/items/item_64467_4/component/file_64468/content">12-
|
||
and 18-Month-Olds Point to Provide Information for Others</a> -
|
||
<strong><em>Journal of Cognition and Development</em></strong>, 2009.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=7322772656439413984&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s10115-006-0062-2">Toward
|
||
understanding the importance of gesture in distributed scientific
|
||
collaboration</a> - <strong><em>Knowledge and Information
|
||
Systems</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="pragmatics">Pragmatics</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/pragmatics/">Pragmatics</a> -
|
||
<strong><em>Plato Stanford</em></strong>. A computational philosophy
|
||
account of Pragmatics, whilch studies utterances in specific
|
||
contexts.</p></li>
|
||
<li><p><a
|
||
href="https://langcog.stanford.edu/papers_new/frank-2012-science.pdf">Predicting
|
||
Pragmatic Reasoning in Language Games</a> -
|
||
<strong><em>Science</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15533081031935746054&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on Rational Speech Act (RSA).</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S136466131630122X">Pragmatic
|
||
Language Interpretation as Probabilistic Inference</a> -
|
||
<strong><em>Trends in Cognitive Sciences</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11393505968563356130&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Noah Goodman and Micheal Frank’s review on Rational
|
||
Speech Act.</p></li>
|
||
<li><p><a
|
||
href="http://cocolab.stanford.edu/papers/BergenLevyGoodman-LexUnc.pdf">Pragmatic
|
||
Reasoning through Semantic Inference</a> - <strong><em>Semantics &
|
||
Pragmatics</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1433855075217315997&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://semantics.uchicago.edu/kennedy/docs/processing.pdf">Processing
|
||
gradable adjectives in context: A visual world study</a> -
|
||
<strong><em>Semantics and Linguistic Theory</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13426776838629402579&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Adjective understanding as a rational inference in the
|
||
context.</p></li>
|
||
<li><p><a
|
||
href="https://transacl.org/index.php/tacl/article/view/1142">Colors in
|
||
Context: A Pragmatic Neural Model for Grounded Language
|
||
Understanding</a> - <strong><em>Transactions of the Association for
|
||
Computational Linguistics</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11119271811833503059&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://compdevlab.yale.edu/docs/2019/2019_ChildDev_Pragmatics.pdf">Social
|
||
Pragmatics: Preschoolers Rely on Commonsense Psychology to Resolve
|
||
Referential Underspecification</a> - <strong><em>Child
|
||
Development</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16352913537004112920&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A piece of evidence for children’s capability on social
|
||
pragmatics.</p></li>
|
||
<li><p><a
|
||
href="http://cocolab.stanford.edu/papers/CohnGordonEtAl2018_NAACL.pdf">Pragmatically
|
||
Informative Image Captioning with Character-Level Inference</a> -
|
||
<strong><em>NAACL’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1670953084401884599&hl=en&as_sdt=2005">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://aclanthology.org/2020.findings-emnlp.173/?ref=https://githubhelp.com">Pragmatic
|
||
Issue-Sensitive Image Captioning</a> - <strong><em>ACL Findings:
|
||
EMNLP’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10608257248144445301&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Application of Rational Speech Act to Image
|
||
Captioning.</p></li>
|
||
<li><p><a
|
||
href="https://cogsci.mindmodeling.org/2019/papers/0091/0091.pdf">Disentangling
|
||
contributions of visual information and interaction history in the
|
||
formation of graphical conventions</a> -
|
||
<strong><em>CogSci’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15046353579508199394&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/s41562-021-01145-1">How
|
||
young children integrate information sources to infer the meaning of
|
||
words</a> - <strong><em>Nature</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://semprag.org/index.php/sp/article/view/sp.5.6/pdf">Information
|
||
Structure in Discourse: Towards an Integrated Formal Theory of
|
||
Pragmatics</a> - <strong><em>Semantics and Pragmatics</em></strong>,
|
||
1998. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9127222314768938599&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s11098-020-01490-3">When
|
||
Lingens meets Frege: communication without common ground</a> -
|
||
<strong><em>Philosophical Studies</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2307.07871">The SocialAI School:
|
||
Insights from Developmental Psychology Towards Artificial Socio-Cultural
|
||
Agents</a> - <strong><em>ICML’23 Workshop on
|
||
Theory-of-Mind</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11933410239580707313&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://sites.google.com/view/socialai-school">Project</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41599-020-0404-9.pdf">Language as
|
||
shaped by the environment: linguistic construal in a collaborative
|
||
spatial task</a> - <strong><em>Nature Humanities and Social Sciences
|
||
Communications</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a href="https://osf.io/sxtaq">Code & Data</a>]. [<a
|
||
href="https://dialoguetoolkit.github.io/chattool/">Dialogue Experimental
|
||
Toolkit(DiET)</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2008.12142">Exploring Urban Form
|
||
Through Openstreetmap Data: A Visual Introduction</a> -
|
||
<strong><em>Urban Experience and Design: Contemporary Perspectives on
|
||
Improving the Public Realm</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7094530618542001733&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a href="https://github.com/gboeing/osmnx">OSMnx
|
||
Tool</a>]. [<a href="https://www.openstreetmap.org/">OpenStreetMap
|
||
Website</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.speech.kth.se/~edlund/bielefeld/references/garrod-and-anderson-1987.pdf">Saying
|
||
what you mean in dialogue: A study in conceptual and semantic
|
||
co-ordination</a> - <strong><em>Cognition</em></strong>, 1987. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15377075954534820544&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://www.sfs.uni-tuebingen.de/~gjaeger/lehre/ws0708/spieltheorie/garrod.pdf">Conversation,
|
||
co-ordination and convention: an empirical investigation of how groups
|
||
establish linguistic conventions</a> -
|
||
<strong><em>Cognition</em></strong>, 1994. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3784850469297049700&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="language-compositionality">Language Compositionality</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/compositionality/">Compositionality</a>
|
||
- <strong><em>Plato Stanford</em></strong>. A computational philosophy
|
||
account on compositionality, one of the distinctive feature of
|
||
language.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/content/pdf/10.1007/BF00763644.pdf">The
|
||
Principle of Semantic Compositionality</a> -
|
||
<strong><em>Topoi</em></strong>, 1994. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10899040818001759322&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on the principle of semantic
|
||
compositionality.</p></li>
|
||
<li><p><a
|
||
href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.3235">On
|
||
The Emergence Of Compositionality</a> - <strong><em>Proceedings of the
|
||
Evolution of Language Conference’06</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16315741180717951222&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on the emergence of
|
||
compositionality.</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1612.07182.pdf">Multi-Agent
|
||
Cooperation and the Emergence of (Natural) Language</a> -
|
||
<strong><em>ICLR’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1931070702879918446&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on the emergence of language in
|
||
multi-agent reinforcement learning.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2017/hash/70222949cc0db89ab32c9969754d4758-Abstract.html">Emergence
|
||
of Language with Multi-agent Games: Learning to Communicate with
|
||
Sequences of Symbols</a> - <strong><em>NeurIPS’18</em></strong>, 2018.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=17308624474306270808&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1804.03980">Emergent communication
|
||
through negotiation</a> - <strong><em>ICLR’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8825869866742501521&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/2019-07481-001">The
|
||
language of generalization</a> - <strong><em>Psychological
|
||
Review</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7723877614160376324&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2004.09124">Compositionality and
|
||
Generalization in Emergent Languages</a> -
|
||
<strong><em>ACL’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5792073344743965767&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://escholarship.org/uc/item/5kv636c5">Word
|
||
formation supports efficient communication: The case of compounds</a> -
|
||
<strong><em>CogSci’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17465553221758916299&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2311.17227">War and Peace
|
||
(WarAgent): Large Language Model-based Multi-Agent Simulation of World
|
||
Wars</a> - 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3598519753107761968&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="coordination">Coordination</h4>
|
||
<ul>
|
||
<li><a href="https://arxiv.org/pdf/2304.14656.pdf">From Explicit
|
||
Communication to Tacit Cooperation: A Novel Paradigm for Cooperative
|
||
MARL</a> - 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12114270828108588849&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="domain-specific-language">Domain Specific Language</h3>
|
||
<h4 id="design-theory">Design Theory</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Domain-specific_language">Domain-Specific
|
||
Language</a> - <strong><em>Wikipedia</em></strong>. Wikipedia
|
||
encyclopedia entry on Domain Specific Languages.</p></li>
|
||
<li><p><a href="https://en.wikipedia.org/wiki/Domain_engineering">Domain
|
||
Engineering</a> - <strong><em>Wikipedia</em></strong>. Wikipedia
|
||
encyclopedia entry on Domain Engineering.</p></li>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/pdf/10.1145/947955.1083808">Epigrams on
|
||
programming</a> - <strong><em>ACM SIGPLAN Notices</em></strong>, 1982.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=6439127299132936476&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://tomassetti.me/domain-specific-languages/">The
|
||
complete guide to (external) Domain Specific Languages</a>. An
|
||
introduction to Domain Specific Languages (DSL) based on 19 DSL
|
||
cases.</p></li>
|
||
<li><p><a
|
||
href="https://people.cs.ksu.edu/~schmidt/505f14/Lectures/WhenDSL.pdf">When
|
||
and How to Develop Domain-Specific Languages</a> - <strong><em>ACM
|
||
Computing Surveys</em></strong>, 2005. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8598236436890577027&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A review on DSL development methodologies that identify
|
||
patterns in the decision, analysis, design, and implementation phases of
|
||
DSL development.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1409.2378">Design Guidelines for
|
||
Domain Specific Languages</a> - <strong><em>OOPSLA Workshop on
|
||
Domain-Specific Modeling (DSM’ 09)</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1962567819031018744&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Guidelines to support a DSL developer to achieve better
|
||
quality of the language design and a better acceptance among its
|
||
users.</p></li>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/abs/10.1145/352029.352035">Domain-specific
|
||
languages: an annotated bibliography</a> - <strong><em>ACM SIGPLAN
|
||
Notices</em></strong>, 2000. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8845429548327315750&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A survey on the topic of domain-specific languages as
|
||
used for the construction and maintenance of software systems.</p></li>
|
||
<li><p><a
|
||
href="http://www-ctp.di.fct.unl.pt/QUASAR/Resources/Papers/2012/Barisic2012SEDES.pdf">Usability
|
||
Evaluation of Domain-Specific Languages</a> -
|
||
<strong><em>ICQICT’12</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3047215455890195199&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An initiative arguing that a systematic approach based on
|
||
User Interface experimental validation techniques should be used to
|
||
assess the impact of new DSLs.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="design-practises">Design Practises</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/abstract/document/9904438">No Grammar
|
||
to Rule Them All: A Survey of JSON-style DSLs for Visualization</a> -
|
||
<strong><em>IEEE Transactions on Visualization and Computer
|
||
Graphics</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17206818917381447796">All
|
||
Versions</a>]. A survey on the design and implementation of 57
|
||
JSON-style DSLs for a variety of visualization and visual interaction
|
||
tasks, suggesting that no one DSL will be able to capture all of them
|
||
without compromising essential parts of its domain design.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0164121214002799">Quantifying
|
||
usability of domain-specific languages: An empirical study on software
|
||
maintenance</a> - <strong><em>Journal of Systems and
|
||
Software</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3450893039446010260&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A study to compare the usability of textual DSLs under
|
||
the perspective of software maintenance, suggesting that the proposed
|
||
metrics were useful: (1) to early identify DSL usability limitations,
|
||
(2) to reveal specific DSL features favoring maintenance tasks, and (3)
|
||
to successfully analyze eight critical DSL usability
|
||
dimensions.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper_files/paper/2022/hash/182aed0379591ebd1d655b2bdc152075-Abstract-Datasets_and_Benchmarks.html">Communicating
|
||
Natural Programs to Humans and Machines</a> -
|
||
<strong><em>NeurIPS’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13381039702346039142&as_sdt=0,5">All
|
||
Versions</a>]. While humans readily generate and interpret instructions
|
||
in a general language, computer systems are shackled to a narrow
|
||
domain-specific language that they can precisely execute. This makes
|
||
building intelligent systems that can generalize to novel situations
|
||
such as ARC difficult. Human-generated instructions are referred as
|
||
`natural programs’. While they resemble computer programs, they are
|
||
distinct in two ways: First, they contain a wide range of primitives;
|
||
Second, they frequently leverage communicative strategies beyond
|
||
directly executable codes.</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/abs/10.1145/3622851">How Domain
|
||
Experts Use an Embedded DSL</a> - <strong><em>OOPSLA’23</em></strong>,
|
||
2023. This work conducts a thematic analysis identified five key themes,
|
||
including: the interaction between the eDSL and the host language has
|
||
significant and sometimes unexpected impacts on eDSL user experience,
|
||
and users preferentially engage with domain-specific communities and
|
||
code templates rather than host language resources.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="domain-specified-applications">Domain Specified
|
||
Applications</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://jbioleng.biomedcentral.com/track/pdf/10.1186/1754-1611-4-13.pdf">Biocoder:
|
||
A programming language for standardizing and automating biology
|
||
protocols</a> - <strong><em>Journal of Biological
|
||
Engineering</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?start=0&hl=en&as_sdt=0,5&cluster=15572197190838916795">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/nmz787/BioCoder">Project</a>]. [<a
|
||
href="https://www.microsoft.com/en-us/download/details.aspx?id=52556">Microsoft
|
||
Page</a>] Microsoft’s programming language for representing biology
|
||
protocols.</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/full/10.1126/science.abd7331">Learning
|
||
the language of viral evolution and escape</a> -
|
||
<strong><em>Science</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=13862653184613223515">All
|
||
Versions</a>]. Natural language processing with two components: grammar
|
||
(or syntax) and meaning (or semantics) for predicting which viral
|
||
mutations may lead to viral escape.</p></li>
|
||
<li><p><a
|
||
href="https://www.biorxiv.org/content/10.1101/2022.12.21.521526v1">A
|
||
high-level programming language for generative protein design</a> -
|
||
2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11732741354610784314&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A high-level programming language based on modular
|
||
building blocks that allows a designer to easily compose a set of
|
||
desired properties. Along with the programming language, there is an
|
||
energy-based generative model, built on atomic resolution structure
|
||
prediction with a language model, that realizes all-atom structure
|
||
designs that have the programmed properties.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s44160-023-00473-6">Universal
|
||
chemical programming language for robotic synthesis repeatability</a> -
|
||
<strong><em>Nature Synthesis</em></strong>, 2024. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3455106495990439366">All
|
||
Versions</a>]. [<a
|
||
href="https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf">Preprint</a>].
|
||
This paper presents an approach that uses a universal chemical
|
||
programming language (χDL) to encode and execute synthesis procedures
|
||
for a variety of chemical reactions, including reductive amination, ring
|
||
formation, esterification, carbon–carbon bond formation and amide
|
||
coupling on four different hardware systems in two laboratories. With
|
||
around 50 lines of code per reaction, the approach uses abstraction to
|
||
efficiently compress chemical protocols.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0950584921002081">iContractML
|
||
2.0: A domain-specific language for modeling and deploying smart
|
||
contracts onto multiple blockchain platforms</a> -
|
||
<strong><em>Information and Software Technology</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1548144959305241494&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A reference model and platform agnostic language for
|
||
smart contracts.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s10994-021-06120-5">Scenic:
|
||
a language for scenario specification and data generation</a> -
|
||
<strong><em>Machine Learning</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13790565080942515865">All
|
||
Versions</a>]. Thie paper proposes a domain-specific language, Scenic,
|
||
for describing scenarios that are distributions over scenes and the
|
||
behaviors of their agents over time. Scenic combines concise, readable
|
||
syntax for spatiotemporal relationships with the ability to
|
||
declaratively impose hard and soft constraints over the
|
||
scenario.</p></li>
|
||
<li><p><a
|
||
href="https://openaccess.thecvf.com/content/CVPR2023/html/Raistrick_Infinite_Photorealistic_Worlds_Using_Procedural_Generation_CVPR_2023_paper.html">Infinite
|
||
Photorealistic Worlds Using Procedural Generation</a> -
|
||
<strong><em>CVPR’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11620922717915489091&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a href="https://infinigen.org/">Website</a>]. [<a
|
||
href="https://openaccess.thecvf.com/content/CVPR2023/supplemental/Raistrick_Infinite_Photorealistic_Worlds_CVPR_2023_supplemental.pdf">Supplementary
|
||
Text</a>]. This paper introduces Infinigen, a procedural generator of
|
||
photorealistic 3D scenes of the natural world. Infinigen is entirely
|
||
procedural: every asset, from shape to texture, is generated from
|
||
scratch via randomized mathematical rules, using no external source and
|
||
allowing infinite variation and composition.</p></li>
|
||
<li><p><a href="https://docs.openlaw.io/">OpenLaw</a> -
|
||
<strong><em>OpenLaw.io</em></strong>. It is now possible to model all or
|
||
parts of legal agreements using code (smart contracts), decreasing the
|
||
cost and friction of creating, securing, and generating binding legal
|
||
agreements. Lawyers lack basic tools to build these dynamic, “smart”
|
||
contracts in a way that is enforceable and understandable to a legal
|
||
professional. OpenLaw is a technology stack to help power next
|
||
generation “smart” legal agreements, with a domain-specific markup
|
||
language, a integration framework, and a series of general
|
||
applications.</p></li>
|
||
<li><p><a href="https://proceedings.mlr.press/v130/lew21a.html">PClean:
|
||
Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic
|
||
Programming</a> - <strong><em>ICML’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2892523061439714130">All
|
||
Versions</a>]. This work presents PClean, a probabilistic programming
|
||
language (PPL) for leveraging dataset-specific knowledge to automate
|
||
Bayesian cleaning, automating Bayesian approaches given the diversity of
|
||
real-world error patterns and the hardness of inference.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="dsl-program-synthesis">DSL Program Synthesis</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/abs/10.1145/3220134.3220135">pix2code:
|
||
Generating Code from a Graphical User Interface Screenshot</a> -
|
||
<strong><em>ACM SIGCHI Symposium on Engineering Interactive Computing
|
||
Systems</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8296741513177971931&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/tonybeltramelli/pix2code">Code</a>]. [<a
|
||
href="https://uizard.io/research/">Website</a>]. This paper shows that
|
||
deep learning methods can be leveraged to train a model end-to-end to
|
||
automatically reverse engineer user interfaces and generate code from a
|
||
single input image with over 77% of accuracy for three different
|
||
platforms (i.e. iOS, Android and web-based technologies).</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2018/hash/6788076842014c83cedadbe6b0ba0314-Abstract.html">Learning
|
||
to Infer Graphics Programs from Hand-Drawn Images</a> -
|
||
<strong><em>NeurIPS’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14065112485794121024&as_sdt=0,5">All
|
||
Versions</a>]. The method learns a model that uses program synthesis
|
||
techniques to recover a graphics program from drawing primitives. These
|
||
programs have constructs like variable bindings, iterative loops, or
|
||
simple kinds of conditionals. With a graphics program in hand, we can
|
||
correct errors made by the deep network and extrapolate
|
||
drawings.</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/abs/10.1145/3571207">babble:
|
||
Learning Better Abstractions with E-Graphs and Anti-unification</a> -
|
||
<strong><em>POPL’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7935064016901049715&as_sdt=0,5">All
|
||
Versions</a>]. This paper proposes library learning modulo theory
|
||
(LLMT), a new library learning algorithm that additionally takes as
|
||
input an equational theory for a given problem domain. LLMT uses
|
||
e-graphs and equality saturation to compactly represent the space of
|
||
programs equivalent modulo the theory, and uses a novel e-graph
|
||
anti-unification technique to find common patterns in the corpus more
|
||
directly and efficiently.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2303.14100">Errors are Useful
|
||
Prompts: Instruction Guided Task Programming with Verifier-Assisted
|
||
Iterative Prompting</a> - 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8063693456660536915">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/ac-rad/xdl-generation">Code</a>]. [<a
|
||
href="https://ac-rad.github.io/clairify/">Website</a>]. This paper
|
||
proposes CLAIRIFY, an approach that combines automatic iterative
|
||
prompting with program verification to ensure programs written in
|
||
data-scarce domain-specific language are syntactically valid and
|
||
incorporate environment constraints.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="problem-solving">Problem Solving</h3>
|
||
<h4 id="human-level-problem-solving">Human-Level Problem Solving</h4>
|
||
<ul>
|
||
<li><p><a href="https://psycnet.apa.org/record/1959-07883-001">Elements
|
||
of a theory of human problem solving</a> - <strong><em>Psychological
|
||
Review</em></strong>, 1958. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6226995019045187501&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Herbert Simon’s original idea on human problem
|
||
solving.</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/1973-10478-000">Human
|
||
Problem Solving</a> - <strong><em>Englewood Cliffs, NJ:
|
||
Prentice-hall</em></strong>, 1972. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3996229083126262536&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Herbert Simon’s classic idea of human problem solving as
|
||
search.</p></li>
|
||
<li><p><a
|
||
href="http://196.223.158.148/bitstream/handle/123456789/2978/596.pdf?sequence=1&isAllowed=y">Learning
|
||
to Solve Problems: A Handbook for Designing Problem-Solving Learning
|
||
Environments</a> - <strong><em>Taylorfrancis</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13262690779319271809&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/abs/10.1126/science.185.4157.1124">Judgment
|
||
under Uncertainty: Heuristics and Biases: Biases in judgments reveal
|
||
some heuristics of thinking under uncertainty</a> -
|
||
<strong><em>Science</em></strong>, 1974. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17040257859216791312&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Daniel Kahneman’s classic idea of prospective
|
||
theory.</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/117/47/29381.short">Computational
|
||
evidence for hierarchically structured reinforcement learning in
|
||
humans</a> - <strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5731363475904675608&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A piece of evidence on hierarchical human
|
||
planning.</p></li>
|
||
<li><p><a
|
||
href="https://www.cnbc.cmu.edu/braingroup/papers/sarafyazd_jazayeri_2019.pdf">Hierarchical
|
||
reasoning by neural circuits in the frontal cortex</a> -
|
||
<strong><em>Science</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9875733886908769773&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Neuroscience evidence supporting rule switch.</p></li>
|
||
<li><p><a href="https://oar.princeton.edu/rt4ds/file/11875/2161">The
|
||
importance of mixed selectivity in complex cognitive tasks</a> -
|
||
<strong><em>Nature</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2707751672275136220&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper introducing mixed selectivity with
|
||
high-dimensional neural representations.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-022-04743-9">People
|
||
construct simplified mental representations to plan</a> -
|
||
<strong><em>Nature</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12068944400080889789&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A computational account on rational problem
|
||
representation in human planning.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661322002819">Goals,
|
||
usefulness and abstraction in value-based choice</a> -
|
||
<strong><em>Trends in Cognitive Sciences</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6256990098976657651&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. A review that outlines the computational and biological
|
||
principles that enable the brain to compute the usefulness of an option
|
||
or action by creating abstractions that flexibly adapt to changing
|
||
goals.</p></li>
|
||
<li><p><a href="https://elifesciences.org/articles/68943">Value signals
|
||
guide abstraction during learning</a> - <strong><em>eLife</em></strong>,
|
||
2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10324834842795908439&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/BF00058926">Learning to
|
||
perceive and act by trial and error</a> - <strong><em>Machine
|
||
Learning</em></strong>, 1991. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1987606770603964473&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog1801_3">Representations
|
||
in distributed cognitive tasks</a> - <strong><em>Cognitive
|
||
Science</em></strong>, 1994. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14781266698447195483">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/S0364021399800226">The
|
||
nature of external representations in problem solving</a> -
|
||
<strong><em>Cognitive Science</em></strong>, 1997. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10698887231200401430&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/pnas/117/47/29302.full.pdf">Rapid
|
||
trail-and-error learning with simulation supports flexible tool use and
|
||
physical reasoning.</a> - <strong><em>Proceedings of the National
|
||
Academy of Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14400178089019636923&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://sites.google.com/view/virtualtoolsgame/home">Project</a>].
|
||
[<a
|
||
href="https://www.pnas.org/content/pnas/suppl/2020/11/20/1912341117.DCSupplemental/pnas.1912341117.sapp.pdf">Appendix</a>].
|
||
A computational account on rapid trail-and-error problem solving with a
|
||
noisy prior model.</p></li>
|
||
<li><p><a
|
||
href="https://cognitivesciencesociety.org/cogsci20/papers/0765/0765.pdf">Abstract
|
||
strategy learning underlies flexible transfer in physical problem
|
||
solving</a> - <strong><em>CogSci’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Abstract+strategy+learning+underlies+flexible+transfer+in+physical+problem+solving.&btnG=">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/forum?id=CXyZrKPz4CU">Physion:
|
||
Evaluating Physical Prediction from Vision in Humans and Machines</a> -
|
||
<strong><em>NeurIPS’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8733318111076645893&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S2352154620301236">Exploration:
|
||
from machines to humans</a> - <strong><em>Current Opinion in Behavioral
|
||
Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8015078432419172621&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S2352154620301467">Balancing
|
||
exploration and exploitation with information and randomization</a> -
|
||
<strong><em>Current Opinion in Behavioral Sciences</em></strong>, 2021.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=8164388137243077863&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0092867421008369">Hippocampal
|
||
neurons construct a map of an abstract value space</a> -
|
||
<strong><em>Cell</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12658820581876003172&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/106/25/10370.short">Insightful
|
||
problem solving and creative tool modification by captive nontool-using
|
||
rooks</a> - <strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6776471679661065229&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://www.pnas.org/content/suppl/2009/05/28/0901008106.DCSupplemental">Supplementary
|
||
Material</a>]. A piece of evidence on creative tool use in intelligent
|
||
animals.</p></li>
|
||
<li><p><a
|
||
href="https://cpilab.org/pubs/Dasgupta2018Learning.pdf">Learning to act
|
||
by integrating mental simulations and physical experiments</a> -
|
||
<strong><em>CogSci’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7342920174595829739&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/ishita-dg/SimulationVSAction">Code</a>].</p></li>
|
||
<li><p><a href="https://gershmanlab.com/pubs/Momennejad17.pdf">The
|
||
successor representation in human reinforcement learning</a> -
|
||
<strong><em>Nature Human Behavior</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7317529612823134939&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="planning">Planning</h4>
|
||
<ul>
|
||
<li><p><a href="https://jair.org/index.php/jair/article/view/11175">From
|
||
Skills to Symbols: Learning Symbolic Representations for Abstract
|
||
High-Level Planning</a> - <strong><em>Journal of Artificial Intelligence
|
||
Research</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17962480659445514879&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Leslie Kaelbling’s review on hierarchical
|
||
Task-and-Motion-Planning (hierarchical TAMP).</p></li>
|
||
<li><p><a
|
||
href="https://www.annualreviews.org/doi/abs/10.1146/annurev-control-091420-084139">Integrated
|
||
Task and Motion Planning</a> - <strong><em>Annual Review of Control,
|
||
Robotics, and Autonomous Systems</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=478421650694199529&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Leslie Kaelbling’s review on Task-and-Motion-Planning
|
||
(TAMP).</p></li>
|
||
<li><p><a
|
||
href="https://dspace.mit.edu/handle/1721.1/126626">Differentiable
|
||
Physics and Stable Modes for Tool-Use and Manipulation Planning</a> -
|
||
<strong><em>Robotics: Science and Systems</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10342169019935480143&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://gershmanlab.com/pubs/Dasgupta18_simulation.pdf">Learning
|
||
to act by integrating mental simulations and physical experiments</a> -
|
||
<strong><em>CogSci’21</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7342920174595829739&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/ftr/10.1111/cogs.12928">What
|
||
Is the Model in Model-Based Planning?</a> - <strong><em>Cognitive
|
||
Science</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10598397017491369972&hl=en&scisbd=1&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2109.11082.pdf">Discovering State
|
||
and Action Abstractions for Generalized Task and Motion Planning</a> -
|
||
<strong><em>AAAI’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=1054368060554971920">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="intrinsic-motivation">Intrinsic Motivation</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2004/hash/4be5a36cbaca8ab9d2066debfe4e65c1-Abstract.html">Intrinsically
|
||
Motivated Reinforcement Learning</a> -
|
||
<strong><em>NeurIPS’04</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9736217847061704054&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review on intrinsic reward functions in
|
||
classic reinforcement learning.</p></li>
|
||
<li><p><a
|
||
href="https://www.frontiersin.org/articles/10.3389/neuro.12.006.2007/full">What
|
||
is intrinsic motivation? A typology of computational approaches</a> -
|
||
<strong><em>Frontiers in Neurorobotics</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11901343819872275353&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.jair.org/index.php/jair/article/view/12087">Adapting
|
||
Behavior via Intrinsic Reward: A Survey and Empirical Study</a> -
|
||
<strong><em>Journal of Artificial Intelligence Research</em></strong>,
|
||
2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5309595875334344707&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.mlr.press/v70/pathak17a.html">Curiosity-driven
|
||
Exploration by Self-supervised Prediction</a> -
|
||
<strong><em>ICML’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9379743003299559904&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on curiosity as intrinsic
|
||
motivation.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1706.01502">UCB Exploration via
|
||
Q-Ensembles</a> - 2017. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=13260404166621290240">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2010.03110">Causal Curiosity: RL
|
||
Agents Discovering Self-supervised Experiments for Causal Representation
|
||
Learning</a> - <strong><em>ICML’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4880520597219138666&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2015/hash/e00406144c1e7e35240afed70f34166a-Abstract.html">Variational
|
||
Information Maximisation for Intrinsically Motivated Reinforcement
|
||
Learning</a> - <strong><em>NeurIPS’15</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9262504233068870193&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on empowerment as intrinsic
|
||
motivation.</p></li>
|
||
<li><p><a href="https://psyarxiv.com/ybs7g/">Intrinsic Exploration as
|
||
Empowerment in a Richly Structured Online Game</a> - 2022. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=12321757821600526668">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://gershmanlab.com/pubs/Tomov21.pdf">Multi-task
|
||
reinforcement learning in humans</a> - <strong><em>Nature Human
|
||
Behavior</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14589018692074515644&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="reinforcement-learning">Reinforcement Learning</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.andrew.cmu.edu/user/rmorina/papers/SuttonBook.pdf">Reinforcement
|
||
learning: An introduction</a> - <strong><em>MIT Press</em></strong>,
|
||
2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8821915215029978039&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Richard Sutton’s comprehensive book on reinforcement
|
||
learning.</p></li>
|
||
<li><p><a
|
||
href="https://www.jair.org/index.php/jair/article/view/10166">Reinforcement
|
||
learning: A survey</a> - <strong><em>Journal of Artificial Intelligence
|
||
Research</em></strong>, 1996. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=4983604491168613713">All
|
||
Versions</a>]. Leslie Kaelbling’s review on reinforcement
|
||
learning.</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2011.00583.pdf">An overview of
|
||
multi-agent reinforcement learning from game theoretical perspective</a>
|
||
- 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16197919002723407603&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Yaodong Yang’s review on multi-agent reinforcement
|
||
learning from the perspective of game theory.</p></li>
|
||
<li><p><a
|
||
href="https://klab.tch.harvard.edu/academia/classes/Neuro230/ReadingAssignments/MnihEtAlHassibis15NatureControlDeepRL.pdf">Human-level
|
||
control through deep reinforcement learning</a> -
|
||
<strong><em>Nature</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12439121588427761338&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on solving Atari games via Deep
|
||
Q-Network.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0004370299000521">Between
|
||
MDPs and semi-MDPs: A framework for temporal abstraction in
|
||
reinforcement learning</a> - <strong><em>Artificial
|
||
Intelligence</em></strong>, 1999. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1471968208408231068&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on operation reinforcement
|
||
learning.</p></li>
|
||
<li><p><a
|
||
href="http://oucsace.cs.ohio.edu/~chelberg/classes/680/paperPresentations/NathanPaperToPresent.pdf">On
|
||
Monte Carlo Tree Search and Reinforcement Learning</a> -
|
||
<strong><em>Journal of Artificial Intelligence Research</em></strong>,
|
||
2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5805718077259491860&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1805.00909">Reinforcement Learning
|
||
and Control as Probabilistic Inference: Tutorial and Review</a> - 2018.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=16437288987337534404&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="http://rail.eecs.berkeley.edu/deeprlcourse-fa18/static/slides/lec-15.pdf">Slides</a>].
|
||
Sergey Levine’s tutorial on treating reinforcement learning
|
||
probabilisticly.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2019/hash/4a46fbfca3f1465a27b210f4bdfe6ab3-Abstract.html">A
|
||
Generalized Algorithm for Multi-Objective Reinforcement Learning and
|
||
Policy Adaptation</a> - <strong><em>NeurIPS’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7721047641895252765&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/forum?id=9SS69KwomAM">Solving
|
||
Compositional Reinforcement Learning Problems via Task Reduction</a> -
|
||
<strong><em>ICLR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15628616147808752058&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8460689">Neural
|
||
Task Programming: Learning to Generalize Across Hierarchical Tasks</a> -
|
||
<strong><em>ICRA’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7155333517647976638&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://academic.oup.com/logcom/article-abstract/28/2/337/4695480">Learning
|
||
to act: qualitative learning of deterministic action models</a> -
|
||
<strong><em>Journal of Logic and Computation</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14570482854600886953&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2109.06076">Learning to Act and
|
||
Observe in Partially Observable Domains</a> - 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2258600434630687063&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2107.06277">Why Generalization in
|
||
RL is Difficult: Epistemic POMDPs and Implicit Partial Observability</a>
|
||
- <strong><em>NeurIPS’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9640851185758072663&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A formal treatment on the generalization problem in
|
||
reinforcement learning.</p></li>
|
||
<li><p><a href="https://openreview.net/forum?id=r1nTpv9eg">Learning to
|
||
Perform Physics Experiments via Deep Reinforcement Learning</a> -
|
||
<strong><em>ICLR’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13142558595749186250&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/abstract/document/9387127">Data-Efficient
|
||
Learning for Complex and Real-Time Physical Problem Solving Using
|
||
Augmented Simulation</a> - <strong><em>Robotics and Automation
|
||
Letters</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3140653562829320759&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.jmlr.org/papers/volume18/16-634/16-634.pdf">A Survey
|
||
of Preference-Based Reinforcement Learning Methods</a> -
|
||
<strong><em>Journal of Machine Learning Research</em></strong>, 2017.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=13278778479251450967&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://papers.NeurIPS.cc/paper/2021/file/4079016d940210b4ae9ae7d41c4a2065-Paper.pdf">On
|
||
the Expressivity of Markov Reward</a> -
|
||
<strong><em>NeurIPS’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4524686816939437211&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A formal treatment of tasks and rewards in reinforcement
|
||
learning modeling.</p></li>
|
||
<li><p><a href="https://proceedings.mlr.press/v37/schulman15.html">Trust
|
||
Region Policy Optimization</a> - <strong><em>ICML’15</em></strong>,
|
||
2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4215501129336400677&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper introducing TRPO, a method for
|
||
optimizing control policies, with guaranteed monotonic
|
||
improvement.</p></li>
|
||
<li><p><a
|
||
href="http://proceedings.mlr.press/v70/achiam17a/achiam17a.pdf">Constrained
|
||
Policy Optimization</a> - <strong><em>ICML’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6114366704163518185&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on constrained reinforcement learning
|
||
(safe reinforcement learning).</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper_files/paper/2019/hash/5faf461eff3099671ad63c6f3f094f7f-Abstract.html">When
|
||
to Trust Your Model: Model-Based Policy Optimization</a> -
|
||
<strong><em>NeurIPS’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4248859125840907707&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://bair.berkeley.edu/blog/2019/12/12/mbpo/">Post</a>].</p></li>
|
||
<li><p><a href="http://proceedings.mlr.press/v139/lee21g.html">SUNRISE:
|
||
A Simple Unified Framework for Ensemble Learning in Deep Reinforcement
|
||
Learning</a> - <strong><em>ICML’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8840831494454574191&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/pokaxpoka/sunrise">Code</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2202.13252">The Quest for a Common
|
||
Model of the Intelligent Decision Maker</a> -
|
||
<strong><em>Multi-disciplinary Conference on Reinforcement Learning and
|
||
Decision Making’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7652784232757502910&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Richard Sutton’s perspective on the future directions of
|
||
reinforcement learning research.</p></li>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/abs/10.5555/3491440.3492111">Automatic
|
||
curriculum learning for deep RL: a short survey</a> -
|
||
<strong><em>IJCAI’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10660055557098312214&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://proceedings.mlr.press/v139/romac21a.html">TeachMyAgent: a
|
||
Benchmark for Automatic Curriculum Learning in Deep RL</a> -
|
||
<strong><em>ICML’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11016662361926634008&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/flowersteam/TeachMyAgent">Project</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="inverse-reinforcement-learning">Inverse Reinforcement
|
||
Learning</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/pdf/10.1145/1015330.1015430">Apprenticeship
|
||
Learning via Inverse Reinforcement Learning</a> -
|
||
<strong><em>ICML’04</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10260011060619377707&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Pieter Abbeel and Andrew Ng’s original paper on inverse
|
||
reinforcement learning (IRL).</p></li>
|
||
<li><p><a
|
||
href="https://www.ijcai.org/Proceedings/07/Papers/416.pdf">Bayesian
|
||
Inverse Reinforcement Learning</a> - <strong><em>IJCAI’07</em></strong>,
|
||
2007. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4154724070362583557&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A Bayesian account on classic inverse reinforcement
|
||
learning.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1902.07742">From Language to
|
||
Goals: Inverse Reinforcement Learning for Vision-Based Instruction
|
||
Following</a> - <strong><em>ICLR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9128320307925997063&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1904.06317.pdf">Few-shot Bayesian
|
||
imitation learning with logical program policies.</a> -
|
||
<strong><em>AAAI’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5103854692762145813&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="http://export.arxiv.org/pdf/2011.09854">Generalized
|
||
Inverse Planning: Learning Lifted non-Markovian Utility for
|
||
Generalizable Task Representation</a> - 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18369106870663956780&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.mlr.press/v139/malik21a.html">Inverse
|
||
Constrained Reinforcement Learning</a> -
|
||
<strong><em>ICML’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Inverse+Constrained+Reinforcement+Learning+S+Malik&btnG=">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="system-1-system-2">System 1 & System 2</h3>
|
||
<h4 id="dual-coding-theory">Dual-Coding Theory</h4>
|
||
<ul>
|
||
<li><p><a href="https://zh.pb1lib.org/book/1004349/825277">Mental
|
||
Representations: A Dual Coding Approach</a> - <strong><em>Oxford
|
||
University Press</em></strong>, 1990. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0,5&q=mental+representations:+a+dual+coding+approach">All
|
||
Versions</a>]. The original book on dual coding theory, in the
|
||
neuroscience account of mental representation.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661321001765">Dual
|
||
coding of knowledge in the human brain</a> - <strong><em>Trends in
|
||
Cognitive Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11751507203561842501&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Yanchao Bi’s review on neuroscience experiments on dual
|
||
coding theory.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0896627320302798">Two
|
||
Forms of Knowledge Representations in the Human Brain</a> -
|
||
<strong><em>Neuron</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=16941965185680116049">All
|
||
Versions</a>]. Illustrating language-derived and sensory-derived
|
||
knowledge.</p></li>
|
||
<li><p><a
|
||
href="http://bilab.bnu.edu.cn/paper/2018/Wang_2018_Cerebral_Cortex.pdf">Organizational
|
||
Principles of Abstract Words in the Human Brain</a> -
|
||
<strong><em>Cerebral Cortex</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=15272192531353715481">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://bilab.bnu.edu.cn/paper/2022/Fu_2022_CC.pdf">Different
|
||
computational relations in language are captured by distinct brain
|
||
systems</a> - <strong><em>Cerebral Cortex</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=720215181903530260&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://europepmc.org/article/med/28190038">The
|
||
Deese-Roediger-McDermott (DRM) task: A simple cognitive paradigm to
|
||
investigate false memories in the laboratory</a> - <strong><em>Journal
|
||
of Visualized Experiments</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10880194606861797581&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://mri-q.com/uploads/3/4/5/7/34572113/gallant_piis0896627312009348.pdf">A
|
||
continuous semantic space describes the representation of thousands of
|
||
object and action categories across the human brain</a> -
|
||
<strong><em>Neuron</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10348115268396987731&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41562-021-01259-6">Rational
|
||
arbitration between statistics and rules in human sequence
|
||
processing</a> - <strong><em>Nature Human Behavior</em></strong>, 2022.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="neural-symbolic-ai">Neural-Symbolic AI</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007/978-3-642-59789-3_58">Regression
|
||
Analysis for Interval-Valued Data</a> - <strong><em>Data Analysis,
|
||
Classification, and Related Methods</em></strong>, 2000. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on symbolic regression.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007/978-3-7908-1709-6_20">Symbolic
|
||
data analysis: what is it?</a> - <strong><em>Proceedings in
|
||
Computational Statistics</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1805.10872">DeepProbLog: Neural
|
||
Probabilistic Logic Programming</a> -
|
||
<strong><em>NeurIPS’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6079567413300944995&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on neuro-symbolic probabilistic
|
||
programming.</p></li>
|
||
<li><p><a
|
||
href="https://www.jair.org/index.php/jair/article/view/11172">Learning
|
||
Explanatory Rules from Noisy Data</a> - <strong><em>Journal of
|
||
Artificial Intelligence Research</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2553893814364678772&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper for differential Inductive Logic
|
||
Programming.</p></li>
|
||
<li><p><a
|
||
href="https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/aaai17lasin.pdf">Combining
|
||
Logical Abduction and Statistical Induction: Discovering Written
|
||
Primitives with Human Knowledge</a> - <strong><em>AAAI’17</em></strong>,
|
||
2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14477085725208589393&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1904.10729.pdf">Neural Logic
|
||
Reinforcement Learning</a> - <strong><em>ICML’19</em></strong>, 2019.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=18074632043038701502&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://papers.NeurIPS.cc/paper/8548-bridging-machine-learning-and-logical-reasoning-by-abductive-learning">Bridging
|
||
Machine Learning and Logical Reasoning by Abductive Learning.</a> -
|
||
<strong><em>NeurIPS’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1518342375288126288&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://daiwz.net/org/slides/ABL-meetup.html#/slide-title">Slides</a>].
|
||
[<a href="https://github.com/AbductiveLearning/ABL-HED">Code</a>]. The
|
||
original paper on Abductive Learning, a derivative-free approach for
|
||
neuro-symbolic learning.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s11432-018-9801-4">Abductive
|
||
learning: towards bridging machine learning and logical reasoning</a> -
|
||
<strong><em>Science China Information Sciences</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2010.03514.pdf">Abductive
|
||
Knowledge Induction From Raw Data</a> -
|
||
<strong><em>IJCAI’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7027142960863064076&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2021/hash/df7e148cabfd9b608090fa5ee3348bfe-Abstract.html">Fast
|
||
Abductive Learning by Similarity-based Consistency Optimization</a> -
|
||
<strong><em>NeurIPS’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8539963460239876225&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An approach for accelerating the convergence of Abductive
|
||
Learning.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2019/file/c20a7ce2a627ba838cfbff082db35197-Paper.pdf">Learning
|
||
by Abstraction: The Neural State Machine</a> -
|
||
<strong><em>NeurIPS’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7361406080192630148&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0004370220301855">Making
|
||
sense of sensory input</a> - <strong><em>Artificial
|
||
Intelligence</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11875529139573472578&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2103.14230v1.pdf">Abstract
|
||
Spatial-Temporal Reasoning via Probabilistic Abduction and Execution</a>
|
||
- <strong><em>CVPR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4172146500538799638&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/pdf?id=SJlh8CEYDB">Learn to
|
||
explain efficiently via neural logic inductive learning</a> -
|
||
<strong><em>ICLR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4550874980727321525&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/gblackout/NLIL">Project</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2006.06649">Closed Loop
|
||
Neural-Symbolic Learning via Integrating Neural Perception, Grammar
|
||
Parsing, and Symbolic Reasoning</a> - <strong><em>ICML’20</em></strong>,
|
||
2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9257372000778020812&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2003.08978">Generating new
|
||
concepts with hybrid neuro-symbolic models.</a> -
|
||
<strong><em>CogSci’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=1912020791698331044">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2006.14448">Learning Task-General
|
||
Representations with Generative Neuro-Symbolic Modeling</a> -
|
||
<strong><em>ICLR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=1335404082385789329">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://clgiles.ist.psu.edu/IST597/materials/slides/papers-memory/2016-graves.pdf">Hybrid
|
||
computing using a neural network with dynamic external memory</a> -
|
||
<strong><em>Nature</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8100274942961380405&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://advances.sciencemag.org/content/6/16/eaay2631/tab-pdf">AI
|
||
Feynman: A physics-inspired method for symbolic regression</a> -
|
||
<strong><em>Science Advances</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3655502646441210453&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A general approach for neuro-symbolic formula
|
||
synthesis.</p></li>
|
||
<li><p><a
|
||
href="http://papers.NeurIPS.cc/paper/8546-classification-by-components-probabilistic-modeling-of-reasoning-over-a-set-of-components.pdf">Classification-by-Components:
|
||
Probabilistic Modeling of Reasoning over a Set of Components</a> -
|
||
<strong><em>NeurIPS’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12691103404451941071&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2006.11524.pdf">Neuro-Symbolic
|
||
Visual Reasoning: Disentangling “Visual” from “Reasoning”</a> -
|
||
<strong><em>ICML’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13160160974887139307&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2020/file/0d82627e10660af39ea7eb69c3568955-Paper.pdf">Understanding
|
||
Deep Architectures with Reasoning Layer</a> -
|
||
<strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=937882599430270789&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1905.10307.pdf">An Explicitly
|
||
Relational Neural Network Architecture</a> -
|
||
<strong><em>ICML’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=37732747764322837&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2103.01937.pdf">Neural Production
|
||
Systems</a> - <strong><em>ICML’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15299280949648915581&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Yoshua Bengio’s perspective on slot attention model as a
|
||
general production system.</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2008.06662.pdf">Compositional
|
||
Generalization via Neural-Symbolic Stack Machines</a> -
|
||
<strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15612498612943317331&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/pdf?id=H1eSS3CcKX">Stochastic
|
||
Optimization of Sorting Networks via Continuous Relaxations</a> -
|
||
<strong><em>ICLR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10619362619006891050&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/pdf?id=BkxUvnEYDH">Program Guided
|
||
Agent</a> - <strong><em>ICLR’20</em></strong>, 2020. [<a
|
||
href="https://openreview.net/forum?id=BkxUvnEYDH">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2020/hash/7a685d9edd95508471a9d3d6fcace432-Abstract.html">Learning
|
||
Compositional Rules via Neural Program Synthesis</a> -
|
||
<strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3160670555314650508&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2006.11287">Discovering Symbolic
|
||
Models from Deep Learning with Inductive Biases</a> -
|
||
<strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9452091824686227240&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1904.11694.pdf">Neural Logic
|
||
Machines</a> - <strong><em>ICLR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4525183211642569463&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1904.12584.pdf">The Neuro-Symbolic
|
||
Concept Learner: Interpreting Scenes, Words, and Sentences From Natural
|
||
Supervision</a> - <strong><em>ICLR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8837128214653317831&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://papers.NeurIPS.cc/paper/2019/file/98d8a23fd60826a2a474c5b4f5811707-Paper.pdf">Visual
|
||
Concept-Metaconcept Learning</a> - <strong><em>NeurIPS’19</em></strong>,
|
||
2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1888051343232298875&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2103.16564">Grounding Physical
|
||
Concepts of Objects and Events Through Dynamic Visual Reasoning</a> -
|
||
<strong><em>ICLR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16735976343684307244&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://jiajunwu.com/papers/toqnet_ijcai.pdf">Temporal
|
||
and Object Quantification Networks</a> -
|
||
<strong><em>IJCAI’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17251222943638414124&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2009.01719.pdf">Grounded Language
|
||
Learning Fast and Slow</a> - <strong><em>ICLR’21</em></strong>, 2021.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=17735027444431750346&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/deepmind/dm_fast_mapping?s=05">Project</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s10994-022-06142-7">Detect,
|
||
Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning
|
||
Framework</a> - <strong><em>Machine Learning</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A neuro-symbolic framework that integrates meta-policy
|
||
learning in inductive logic programming.</p></li>
|
||
<li><p><a
|
||
href="https://openaccess.thecvf.com/content/CVPR2023/html/Gupta_Visual_Programming_Compositional_Visual_Reasoning_Without_Training_CVPR_2023_paper.html">Visual
|
||
Programming: Compositional Visual Reasoning Without Training</a> -
|
||
<strong><em>CVPR’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16156060658942400125&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. VISPROG, a neuro-symbolic approach to solving complex and
|
||
compositional visual tasks given natural language instructions, using
|
||
the in-context learning ability of large language models to generate
|
||
python-like modular programs, which are then executed to get both the
|
||
solution and a comprehensive and interpretable rationale.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="explainability">Explainability</h3>
|
||
<h4 id="trustworthy-ai">Trustworthy AI</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.pnas.org/doi/full/10.1073/pnas.2111547119">Bayesian
|
||
modeling of human–AI complementarity</a> - <strong><em>Proceedings of
|
||
the National Academy of Sciences</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15735143859968841009&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A Bayesian framework for combining the predictions and
|
||
different types of confidence scores from humans and machines.</p></li>
|
||
<li><p><a
|
||
href="https://yzhu.io/publication/openbottle2019scirob/paper.pdf">A tale
|
||
of two explanations: Enhancing human trust by explaining robot
|
||
behavior</a> - <strong><em>Science Robotics</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=3985046411399524590">All
|
||
Versions</a>]. The original paper on human believable robot, a result of
|
||
the DAPAR-XAI.</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1909.06907.pdf">X-ToM: Explaining
|
||
with Theory-of-Mind for Gaining Justified Human Trust</a> - 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7751326666821697923&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Introducing the idea of AI estimating human users’
|
||
knowledge in to explainable AI.</p></li>
|
||
<li><p><a
|
||
href="https://ojs.aaai.org/index.php/AAAI/article/view/5643">CoCoX:
|
||
Generating Conceptual and Counterfactual Explanations via
|
||
Fault-Lines</a> - <strong><em>AAAI’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17443137068166403183&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S2589004221015510">CX-ToM:
|
||
Counterfactual explanations with theory-of-mind for enhancing human
|
||
trust in image recognition models</a> -
|
||
<strong><em>iScience</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17526041764295337444">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="strong-machine-learning">Strong Machine Learning</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s10994-018-5707-3">Ultra-Strong
|
||
Machine Learning: comprehensibility of programs learned with ILP</a> -
|
||
<strong><em>Machine Learning</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Stephen Muggleton’s account of ultra-strong machine
|
||
learning, which not only learns human understandable knowledge, but also
|
||
improves human performance on the corresponding tasks.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007%2Fs10994-020-05941-0">Beneficial
|
||
and harmful explanatory machine learning</a> - <strong><em>Machine
|
||
Learning</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16983722694047294963&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.ijcai.org/Proceedings/2017/497">Deep Forest:
|
||
Towards An Alternative to Deep Neural Networks</a> -
|
||
<strong><em>IJCAI’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7391596872731517007&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/LAMDA-NJU/Deep-Forest">Project</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2004.00221">NBDT: Neural-Backed
|
||
Decision Trees</a> - <strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1902399007162005819&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/alvinwan/neural-backed-decision-trees">Code</a>].
|
||
Expliciting the decision process of a decision tree through neural
|
||
networks.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="explainable-deep-learning">Explainable Deep Learning</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://github.com/jacobgil/pytorch-grad-cam">pytorch-grad-cam</a>
|
||
- 2021. Class Activation Map methods implemented in Pytorch, with many
|
||
elegant features.</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/8099837">Network
|
||
dissection: Quantifying interpretability of deep visual
|
||
representations</a> - <strong><em>CVPR’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18069685615852396783&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a href="http://netdissect.csail.mit.edu/">Project</a>].
|
||
[<a href="http://places2.csail.mit.edu/index.html">Dataset:
|
||
Places365</a>]. The original paper on visualizing the class activation
|
||
maps to explain convolutional neural networks.</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/pnas/early/2020/08/31/1907375117.full.pdf">Understanding
|
||
the role of Individual Units in a Deep Neural Network</a> -
|
||
<strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11996680970579301810&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. David Bau’s review on network dissection for
|
||
discriminative and generative models.</p></li>
|
||
<li><p><a href="https://distill.pub/2020/circuits/zoom-in/">Zoom In: An
|
||
Introduction to Circuits</a> - <strong><em>Distill</em></strong>, 2020.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=9053581372570691569&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A perspective on treating neural networks as
|
||
circuits.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2020/hash/c74956ffb38ba48ed6ce977af6727275-Abstract.html">Compositional
|
||
Explanations of Neurons</a> - <strong><em>NeurIPS’20</em></strong>,
|
||
2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15725346730266402738&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/jayelm/compexp">Project</a>]. A
|
||
concept-composition version of network dissection.</p></li>
|
||
<li><p><a
|
||
href="http://papers.NeurIPS.cc/paper/9095-this-looks-like-that-deep-learning-for-interpretable-image-recognition.pdf">This
|
||
Looks Like That: Deep Learning for Interpretable Image Recognition</a> -
|
||
<strong><em>NeurIPS’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9461838581952136719&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/pnas/116/16/7723.full.pdf">Unsupervised
|
||
learning by competing hidden units</a> - <strong><em>Proceedings of the
|
||
National Academy of Sciences</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1228003598355915526&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2006.09994.pdf">Noise or Signal:
|
||
The Role of Backgrounds in Image Classification</a> -
|
||
<strong><em>ICLR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14729938011425134088&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/MadryLab/backgrounds_challenge">Code &
|
||
Data</a>]. [<a
|
||
href="https://gradientscience.org/background/">Project</a>]. A
|
||
perspective on image background provides strong clue for foreground
|
||
classification.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2018/hash/5fc34ed307aac159a30d81181c99847e-Abstract.html">Towards
|
||
Understanding Learning Representations: To What Extent Do Different
|
||
Neural Networks Learn the Same Representation</a> -
|
||
<strong><em>NeurIPS’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=401428033641216502&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Maching the learned pattern of neurons in different
|
||
neural networks.</p></li>
|
||
<li><p><a
|
||
href="https://kriegeskortelab.zuckermaninstitute.columbia.edu/sites/default/files/content/MehrerKietzmann_2020_NatureComms.pdf">Individual
|
||
differences among deep neural network models</a> - <strong><em>Nature
|
||
Communications</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8259893575188417318&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="embodied-intelligence">Embodied Intelligence</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/embodied-cognition/">Embodied
|
||
Cognition</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on Embodied Cognition, which emphasizes
|
||
the significance of an agent’s physical body in cognitive
|
||
abilities.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/content-externalism/">Externalism
|
||
About the Mind</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on mind externalism, a long-term debate
|
||
about the boundary of embodied intelligence.</p></li>
|
||
<li><p><a
|
||
href="https://www.researchgate.net/profile/David-Woods-19/publication/242545872_Cognitive_Engineering_Human_Problem_Solving_with_Tools/links/542becf70cf29bbc126ac097/Cognitive-Engineering-Human-Problem-Solving-with-Tools.pdf">Cognitive
|
||
engineering: Human problem solving with tools</a> - <strong><em>Human
|
||
Factors</em></strong>, 1988. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14194840995416222723&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original idea of investigating huamn tool use in
|
||
problem solving.</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/1993-97340-000">Tools,
|
||
language and cognition in human evolution</a> - <strong><em>Cambridge
|
||
University Press</em></strong>, 1993. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6046350461147957220&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A classic perspective correlating human tool use with the
|
||
evolution of civilization.</p></li>
|
||
<li><p><a
|
||
href="https://icds.uoregon.edu/wp-content/uploads/2014/06/Clark-and-Chalmers-The-Extended-Mind.pdf">The
|
||
Extended Mind</a> - <strong><em>Analysis</em></strong>, 1998. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9546561188261943866&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on the debate of mind
|
||
externalism.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661303003231">The
|
||
neural bases of complex tool use in humans</a> - <strong><em>Trends in
|
||
Cognitive Sciences</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3612212926196611828&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A neuroscience account of human tool use.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0960982207017708">Spontaneous
|
||
Metatool Use by New Caledonian Crows</a> - <strong><em>Current
|
||
Biology</em></strong>, 2007. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9263531730425342443&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A piece of evidence that intelligent animals can take
|
||
advantage of matatools to make tools for problem solving.</p></li>
|
||
<li><p><a
|
||
href="https://journals.sagepub.com/doi/abs/10.1177/0956797610371962">Rapid
|
||
Assimilation of External Objects Into the Body Schema</a> -
|
||
<strong><em>Psychological Science</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=854636910326733489&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.eva.mpg.de/documents/Cambridge/Tennie_Cultural_BehBrainSci_2012_1566208.pdf">The
|
||
cognitive bases of human tool use</a> - <strong><em>Behavioral and Brain
|
||
Sciences</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4648150119820414671&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00214/full">The
|
||
embodied mind extended: using words as social tools</a> -
|
||
<strong><em>Frontiers in Psychology</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14719988081062606352&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://royalsocietypublishing.org/doi/10.1098/rstb.2012.0408">Tool
|
||
use as adaptation</a> - <strong><em>Philosophical Transactions of the
|
||
Royal Society B: Biological Sciences</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8060841461200774807&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0028393214000232">Intensive
|
||
tool-practice and skillfulness facilitate the extension of body
|
||
representations in humans</a> -
|
||
<strong><em>Neuropsychologia</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10578024091098127929&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://psycnet.apa.org/doiLanding?doi=10.1037%2Frev0000027">Tool
|
||
use and affordance: Manipulation-based versus reasoning-based
|
||
approaches</a> - <strong><em>Psychological Review</em></strong>, 2016.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=3284942486402374505&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A classic review on human tool use and
|
||
affordance.</p></li>
|
||
<li><p><a href="https://escholarship.org/uc/item/5gf0m7x3">Meta-strategy
|
||
learning in physical problem-solving: the effect of embodied
|
||
experience</a> - <strong><em>CogSci’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=9713842177532954702">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://yzhu.io/publication/tool2015cvpr/paper.pdf">Understanding
|
||
Tools: Task-Oriented Object Modeling, Learning and Recognition</a> -
|
||
<strong><em>CVPR’15</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4609926671953500969&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://yzhu.io/publication/tool2015cvpr/">Project</a>]. The
|
||
original paper introducing affordance and physically-grounded tool use
|
||
into computer vision.</p></li>
|
||
<li><p><a
|
||
href="https://robotics.sciencemag.org/content/6/54/eabd7935.abstract">Robotic
|
||
hand augmentation drives changes in neural body representation</a> -
|
||
<strong><em>Science Robotics</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1622125726197763917&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.jneurosci.org/content/jneuro/41/13/2980.full.pdf">Expert
|
||
Tool Users Show Increased Differentiation between Visual Representations
|
||
of Hands and Tools</a> - <strong><em>Journal of
|
||
Neuroscience</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13454164767827515188&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2106.05654.pdf">Visual scoping
|
||
operations for physical assembly</a> -
|
||
<strong><em>CogSci’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7238090583833839&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.cc.gatech.edu/ai/robot-lab/online-publications/StoytchevICRA2005.pdf">Behavior-grounded
|
||
representation of tool affordances</a> -
|
||
<strong><em>ICRA’05</em></strong>, 2005. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6115815663915603675&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007/978-3-642-38812-5_1">A
|
||
Relational Approach to Tool-Use Learning in Robots</a> -
|
||
<strong><em>ILP’12</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s10514-017-9637-x">Relational
|
||
affordances for multiple-object manipulation</a> -
|
||
<strong><em>Autonomous Robots</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://m.roboticsproceedings.org/rss15/p01.pdf">Improvisation
|
||
through Physical Understanding: Using Novel Objects as Tools with Visual
|
||
Foresight</a> - <strong><em>RSS’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4316276917607326251&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.biorxiv.org/content/10.1101/2021.07.08.451333v1">Meta-strategy
|
||
learning in physical problem-solving: the effect of embodied
|
||
experience</a> - 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9713842177532954702&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2002.06289">3D dynamic scene
|
||
graphs: Actionable spatial perception with places, objects, and
|
||
humans</a> - <strong><em>RSS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4428742298455436054&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A system for modeling 3D dynamic scene graphs on multiple
|
||
levels (metric-semantic mesh, objects and agents, places and structures,
|
||
rooms, and buildings).</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="evolutionary-intelligence">Evolutionary Intelligence</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="http://websites.umich.edu/~zhanglab/clubPaper/06_08_2012.pdf">Evolutionary
|
||
trade-offs, Pareto optimality, and the geometry of phenotype space</a> -
|
||
<strong><em>Science</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16162252507845975080&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A classic paper correlating biological trade-offs with
|
||
the evolution of pareto optimality.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/BF01442131">Pareto
|
||
optimality in multiobjective problems</a> - <strong><em>Applied
|
||
Mathematics and Optimization</em></strong>, 1977. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on the pareto optimality in
|
||
multiobjective problems.</p></li>
|
||
<li><p><a href="http://www.soft-computing.de/SMC0805.pdf">Pareto-Based
|
||
Multiobjective Machine Learning: An Overview and Case Studies</a> -
|
||
<strong><em>IEEE Transactions on Systems, Man, and
|
||
Cybernetics</em></strong>, 2008. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11308312498510305429&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review on the application of pareto
|
||
optimality to multiobjective machine learning.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-019-1153-z">Phylogenetic
|
||
evidence for Sino-Tibetan origin in northern China in the Late
|
||
Neolithic</a> - <strong><em>Nature</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A Bayesian phylogenetic analysis on two competing
|
||
hypotheses of the origin of the Sino-Tibetan language family suggests
|
||
that the initial expansion of Sino-Tibetan languages occurred
|
||
approximately 4,000–6,000 years before present (BP; taken as AD 1950) in
|
||
the Yellow River basin of northern China, and that this expansion is
|
||
associated with the development of the Yangshao and/or Majiayao
|
||
Neolithic cultures.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-021-04108-8">Triangulation
|
||
supports agricultural spread of the Transeurasian languages</a> -
|
||
<strong><em>Nature</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://www.nature.com/articles/d41586-021-03037-w">Nature
|
||
News</a>]. A triangulation of linguistic, archaeological and genetic
|
||
data suggests that the Transeurasian language family originated in a
|
||
population of grain farmers in China around 9,000 years ago, and that
|
||
agriculture underpinned its spread.</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/abs/10.1126/science.ade7981">From
|
||
language development to language evolution: A unified view of human
|
||
lexical creativity</a> - <strong><em>Science</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15871163761816546924&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://brochhagen.github.io/content/ms/accepted-lexical-creativity.pdf">Preprint</a>].
|
||
This work supports a unified foundation for human lexical creativity
|
||
underlying both the fleeting products of individual ontogeny and the
|
||
evolutionary products of phylogeny across languages.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="methodologies-for-experiments">Methodologies for
|
||
Experiments</h3>
|
||
<h4 id="quantitative-analysis">Quantitative Analysis</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="http://www.jakebowers.org/ITVExperiments/angristimbensrubin96.pdf">Identification
|
||
of Causal Effects Using Instrumental Variables</a> - <strong><em>Journal
|
||
of the American Statistical Association</em></strong>, 1996. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17166265099721941605">All
|
||
Versions</a>]. The original paper on Instrumental Variables for natural
|
||
sociology studies.</p></li>
|
||
<li><p><a
|
||
href="https://www.annualreviews.org/doi/abs/10.1146/annurev-psych-122414-033702">Experiments
|
||
with More Than One Random Factor: Designs, Analytic Models, and
|
||
Statistical Power</a> - <strong><em>Annual Review of
|
||
Psychology</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6652444619934494760&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review of the quantitative analysis
|
||
techniques for behavioral studies.</p></li>
|
||
<li><p><a
|
||
href="https://mpra.ub.uni-muenchen.de/4823/1/MPRA_paper_4823.pdf">With
|
||
or Without U? The Appropriate Test for a U-Shaped Relationship</a> -
|
||
<strong><em>Oxford Bulletin of Economics and Statistics</em></strong>,
|
||
2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1574723532506536904&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original method for testing U-shape relation from the
|
||
data, which is distinctive from the quadratic regression test.</p></li>
|
||
<li><p><a
|
||
href="https://journals.sagepub.com/doi/pdf/10.1177/2515245918805755">Two
|
||
lines: A valid alternative to the invalid testing of U-shaped
|
||
relationships with quadratic regressions</a> - <strong><em>Advances in
|
||
Methods and Practices in Psychological Science</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12010185803500406162&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An alternative method to test the statistical
|
||
significance of U-shaped relationships.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="scaling-up-behavioral-studies">Scaling Up Behavioral
|
||
Studies</h4>
|
||
<ul>
|
||
<li><p><a href="https://osf.io/wksv8">Scaling up experimental social,
|
||
behavioral, and economic science</a> - <strong><em>Open Science
|
||
Foundation Preprints</em></strong>. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Scaling+up+experimental+social%2C+behavioral%2C+and+economic+science&btnG=">All
|
||
Versions</a>]. A white paper on scaling up social, behavioral, and
|
||
econimic experiments.</p></li>
|
||
<li><p><a
|
||
href="https://scholar.harvard.edu/files/henrich/files/henrich_heine_norenzayan_2010-2.pdf">The
|
||
weirdest people in the world?</a> - <strong><em>Brain and Behavioral
|
||
Sciences</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3129419557801277936&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on rethinking and tackling the sample
|
||
bias in behaivoral studies, where most subjects are drawn from Western,
|
||
Educated, Industrialized, Rich, and Democratic (WEIRD)
|
||
societies.</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/doi/10.1073/pnas.1915841117">Scaling up
|
||
psychology via Scientific Regret Minimization</a> -
|
||
<strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8011895688226766944&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The statistical and ecological basis for scaling up
|
||
behavioral studies.</p></li>
|
||
<li><p><a
|
||
href="https://cpb-us-w2.wpmucdn.com/web.sas.upenn.edu/dist/a/511/files/2021/06/Bhatia-He-Science.pdf">Machine-generated
|
||
theories of human decision-making</a> -
|
||
<strong><em>Science</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7065547001880027350&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://cocosci.princeton.edu/jpeterson/papers/peterson2021-science.pdf">Using
|
||
large-scale experiments and machine learning to discover theories of
|
||
human decision-making</a> - <strong><em>Science</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7456250222852859810&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A piece of evidence for the merits brought by large-scale
|
||
behavioral studies in social science.</p></li>
|
||
<li><p><a
|
||
href="http://jakehofman.com/pdfs/integrating-prediction-and-explanation.pdf">Integrating
|
||
explanation and prediction in computational social science</a> -
|
||
<strong><em>Nature</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=288245575125750925&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://cocosci.princeton.edu/josh/papers/griffiths-largeimagedatabases-topics2016.pdf">Exploring
|
||
human cognition using large image databases</a> - <strong><em>Topics in
|
||
Cognitive Sciences</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3629906005701226294&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://web.archive.org/web/20170809024454id_/http://www.kevinjing.com/visual_search_at_pinterest.pdf">Visual
|
||
Search at Pinterest</a> - <strong><em>KDD’15</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2051024301293529405&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Large scale user study in the development of the
|
||
recommendations system by Pinterest.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="decision-making">Decision Making</h4>
|
||
<ul>
|
||
<li><a
|
||
href="https://link.springer.com/article/10.3758/s13428-022-01789-5">A
|
||
computational process-tracing method for measuring people’s planning
|
||
strategies and how they change over time</a> - <strong><em>Behavior
|
||
Research Methods</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041">All
|
||
Versions</a>]. Model-based strategy identification.</li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="question-answering">Question Answering</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://cogsci.mindmodeling.org/2016/papers/0122/paper0122.pdf">Searching
|
||
large hypothesis spaces by asking questions</a> -
|
||
<strong><em>CogSci’16</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3398849603439166012&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A behavioral study for the 20 questions game.</p></li>
|
||
<li><p><a
|
||
href="https://gureckislab.org/papers/RotheLakeGureckis-2016cogsci.pdf">Asking
|
||
and evaluating natural language questions</a> -
|
||
<strong><em>CogSci’16</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=34641833161282231&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A behavioral study for the battleship game.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s42113-018-0005-5">Do
|
||
People Ask Good Questions?</a> - <strong><em>Computational Brain &
|
||
Behavior</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://nyuccl.org/papers/Rothe-Lake-Gureckis-2019-Cogsci.pdf">Asking
|
||
goal-oriented questions and learning from answers</a> -
|
||
<strong><em>CogSci’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14185546187726917682&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="human-machine-comparison">Human-Machine Comparison</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://psycnet.apa.org/record/1973-00249-001">Elimination by
|
||
aspects: A theory of choice</a> - <strong><em>Psychological
|
||
Review</em></strong>, 1972. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1633792484482810297&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Herbert Simon’s early experiments on computer aided
|
||
behavioral studies.</p></li>
|
||
<li><p><a
|
||
href="https://stacks.stanford.edu/file/druid:qv796fc9687/qv796fc9687.pdf">Problem
|
||
Solving and Rule Induction: A Unified View</a> - <strong><em>Knowledge
|
||
and cognition</em></strong>, 1974. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12943734683291006234&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/112/37/11708.short">Evidence
|
||
integration in model-based tree search</a> - <strong><em>Proceedings of
|
||
the National Academy of Sciences</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11085043350027609187&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/content/pdf/10.1007/s42113-019-00053-y.pdf">People
|
||
Infer Recursive Visual Concepts from Just a Few Examples</a> -
|
||
<strong><em>Computational Brain & Behavior</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3871396883970734141&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://escholarship.org/content/qt3xf2n3vc/qt3xf2n3vc.pdf">One-shot
|
||
learning of generative speech concepts</a> -
|
||
<strong><em>CogSci’14</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15482292457660075957&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1901.04587">Human few-shot
|
||
learning of compositional instructions</a> -
|
||
<strong><em>CogSci’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12841163907815018136&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2103.05823.pdf">Fast and flexible:
|
||
Human program induction in abstract reasoning tasks</a> -
|
||
<strong><em>CogSci’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5294483826040237516&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://proceedings.mlr.press/v80/dubey18a.html">Investigating
|
||
Human Priors for Playing Video Games</a> -
|
||
<strong><em>ICML’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2202192690517876762&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S2352154619300622">Tasks
|
||
for aligning human and machine planning</a> - <strong><em>Current
|
||
Opinion in Behavioral Sciences</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8308872468787875598&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://perception.jhu.edu/files/PDFs/19_Adversarial_Deciphering/ZhouFirestone-AdversarialDeciphering.pdf">Humans
|
||
can decipher adversarial images</a> - <strong><em>Nature
|
||
Communications</em></strong>. 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4423950118844131054&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41593-022-01026-4.pdf">Shared
|
||
computational principles for language processing in humans and deep
|
||
language models</a> - <strong><em>Nature Neuroscience</em></strong>,
|
||
2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="association-test">Association Test</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Implicit-association_test">Implicit
|
||
Association Test</a> - <strong><em>Wikipedia</em></strong>. Wikipedia on
|
||
the Implicit Association Test, a controversial assessment intended to
|
||
detect subconscious associations between mental representations of
|
||
objects (concepts) in memory.</p></li>
|
||
<li><p><a
|
||
href="http://faculty.fortlewis.edu/burke_b/Senior/BLINK%20replication/IAT.pdf">Measuring
|
||
Individual Differences in Implicit Cognition: The Implicit Association
|
||
Test</a> - <strong><em>Journal of Personality and Social
|
||
Psychology</em></strong>, 1998. [<a
|
||
href="https://scholar.google.com/scholar?cluster=302378224541015580&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper introducing the Implicit Association
|
||
Test.</p></li>
|
||
<li><p><a
|
||
href="http://faculty.washington.edu/agg/pdf/Gwald_Nosek_ZEITSCHR_2001.OCR.pdf">Health
|
||
of the Implicit Association Test at age 3</a> - <strong><em>Zeitschrift
|
||
für Experimentelle Psychologie</em></strong>, 2001. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10868478693422595588&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The 3rd year review for the IAT.</p></li>
|
||
<li><p><a
|
||
href="https://faculty.washington.edu/agg/pdf/Nosek%20&%20al.IATatage7.2007.pdf">The
|
||
Implicit Association Test at Age 7: A Methodological and Conceptual
|
||
Review</a> - <strong><em>Social psychology and the unconscious: The
|
||
automaticity of higher mental processes (pp. 265–292), Psychology
|
||
Press</em></strong>, 2007. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16189750920013376566&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The 7th year review for the IAT.</p></li>
|
||
<li><p><a
|
||
href="http://faculty.washington.edu/agg/IATmaterials/PDFs/Hofmann%20&%20al%20(PSPB,2005).pdf">A
|
||
Meta-Analysis on the Correlation Between the Implicit Association Test
|
||
and Explicit Self-Report Measures</a> - <strong><em>Personality and
|
||
Social Psychology Bulletin</em></strong>, 2005. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4888328728717829047&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="virtual-reality">Virtual Reality</h4>
|
||
<ul>
|
||
<li><p><a href="https://www.nature.com/articles/nn948">Virtual reality
|
||
in behavioral neuroscience and beyond</a> - <strong><em>Nature
|
||
Neuroscience</em></strong>, 2002. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A classic review on the early applications of Virtual
|
||
Reality to behavioral studies.</p></li>
|
||
<li><p><a
|
||
href="https://stanfordvr.com/mm/2009/fox-jmp-vr-survival.pdf">Virtual
|
||
reality: A survival guide for the social scientist</a> -
|
||
<strong><em>Journal of Media Psychology</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17318470193315023264&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/2022-60836-006">The
|
||
psychology of virtual reality</a> - <strong><em>The psychology of
|
||
technology: Social science research in the age of Big Data
|
||
(pp. 155–193), American Psychological Association</em></strong>, 2022.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=11535480055596209683&hl=en&as_sdt=0,5&as_ylo=2021">All
|
||
Versions</a>]. Jeremy Bailenson’s review on the applications of Virtual
|
||
Reality to behavioral studies.</p></li>
|
||
<li><p><a
|
||
href="https://stanfordvr.com/mm/2015/cummings-mp-how-immersive.pdf">How
|
||
Immersive Is Enough? A Meta-Analysis of the Effect of Immersive
|
||
Technology on User Presence</a> - <strong><em>Media
|
||
Psychology</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9218122072360464558&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A meta-analysis on the extent to which technologies need
|
||
to be immersive in order to generate a sense of presence.</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/10108427">Towards
|
||
an Understanding of Distributed Asymmetric Collaborative Visualization
|
||
on Problem-solving</a> - <strong><em>VR’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11228377215337222005&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="meta-level-considerations">Meta-Level Considerations</h3>
|
||
<h4 id="meta-learning">Meta Learning</h4>
|
||
<ul>
|
||
<li><p><a href="https://arxiv.org/pdf/2201.03916.pdf">Automated
|
||
Reinforcement Learning (AutoRL): A Survey and Open Problems</a> - 2022.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=9025378857688824887&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review on AutoRL.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.mlr.press/v70/finn17a/finn17a.pdf">Model-Agnostic
|
||
Meta-Learning for Fast Adaptation of Deep Networks</a> -
|
||
<strong><em>ICML’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17278604844873996878">All
|
||
Versions</a>]. [<a
|
||
href="https://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/">Post</a>].
|
||
Chelsea Finn’s original paper on Model-Agnostic Meta-Learning
|
||
(MAML).</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2018/hash/e1021d43911ca2c1845910d84f40aeae-Abstract.html">Bayesian
|
||
Model-Agnostic Meta-Learning</a> - <strong><em>NeurIPS’18</em></strong>,
|
||
2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7370333111335795917&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A Bayesian account on MAML.</p></li>
|
||
<li><p><a
|
||
href="https://openreview.net/forum?id=SJeD3CEFPH">Meta-Q-Learning</a> -
|
||
<strong><em>ICLR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2865388954464396222&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The milestone paper on context Meta-RL.</p></li>
|
||
<li><p><a
|
||
href="http://proceedings.mlr.press/v97/rakelly19a.html">Efficient
|
||
Off-Policy Meta-Reinforcement Learning via Probabilistic Context
|
||
Variables</a> - <strong><em>ICML’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15379570585451726919&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/forum?id=TQt98Ya7UMP">Balancing
|
||
Constraints and Rewards with Meta-Gradient D4PG</a> -
|
||
<strong><em>ICLR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2805226315118298313&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/forum?id=Bk8BvDqex">Metacontrol
|
||
for Adaptive Imagination-Based Optimization</a> -
|
||
<strong><em>ICLR’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16728474512617398730&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2021/hash/1e4d36177d71bbb3558e43af9577d70e-Abstract.html">On
|
||
Effective Scheduling of Model-based Reinforcement Learning</a> -
|
||
<strong><em>NeurIPS’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11128521607771619105&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="marrs-levels-of-analysis">Marr’s Levels of Analysis</h4>
|
||
<ul>
|
||
<li><p><a href="https://usa1lib.org/book/1223444/8e5ca8">Vision: A
|
||
Computational Investigation into the Human Representation and Processing
|
||
of Visual Information</a> - <strong><em>MIT Press</em></strong>, 1982.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=14386368570811483142&hl=en&as_sdt=0,44">All
|
||
Versions</a>]. David Marr’s original book on the levels of
|
||
analysis.</p></li>
|
||
<li><p><a
|
||
href="https://dspace.mit.edu/bitstream/handle/1721.1/5782/AIM-357.pdf?sequence=2">From
|
||
understanding computation to understanding neural circuitry</a> -
|
||
<strong><em>Neuroscience Research Program Bulletin</em></strong>, 1979.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?start=0&hl=en&as_sdt=0,5&cluster=11150567121969913334">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://cocosci.princeton.edu/tom/papers/LabPublications/BridgingLevelsAnalysis.pdf">Bridging
|
||
Levels of Analysis for Probabilistic Models of Cognition</a> -
|
||
<strong><em>Current Directions in Psychological Science</em></strong>,
|
||
2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5063382112136991296&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A Marr’s paradigm account on probabilistic
|
||
models.</p></li>
|
||
<li><p><a
|
||
href="https://people.csail.mit.edu/pkrafft/papers/krafft-griffiths-levels-css.pdf">Levels
|
||
of Analysis in Computational Social Science</a> -
|
||
<strong><em>CogSci’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10178929388985626844&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A Marr’s paradigm account on computational social
|
||
science.</p></li>
|
||
<li><p><a href="https://baicsworkshop.github.io/pdf/BAICS_6.pdf">Levels
|
||
of Analysis for Machine Learning</a> - <strong><em>ICLR’20 Bridging AI
|
||
and Cognitive Science Workshop</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13819038971626384115&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A Marr’s paradigm account on machine learning.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="gestalt">Gestalt</h4>
|
||
<ul>
|
||
<li><p><a href="https://psycnet.apa.org/record/2007-10344-001">Gestalt
|
||
theory</a> - <strong><em>A source book of Gestalt
|
||
psychology</em></strong>, 1938. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18133275659218646817&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original book on Gestalt psychology.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/BF00422382">Gestalt
|
||
Psychology</a> - <strong><em>Psychologische Forschung</em></strong>,
|
||
1967. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16023098380090751616&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Wolfgang Köhler’s review on Gestalt psychology.</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9450.1984.tb01001.x">Restructuring
|
||
revisited I. Summary and critique of the Gestalt theory of problem
|
||
solving</a> - <strong><em>Scandinavian Journal of
|
||
Psychology</em></strong>, 1984. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1540079499182933565&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9450.1984.tb01005.x">Restructuring
|
||
revisited II. An information processing theory of restructuring and
|
||
insight</a> - <strong><em>Scandinavian Journal of
|
||
Psychology</em></strong>, 1984. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1821980539002417470&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/1993-36184-001">Thoughts
|
||
beyond words: When language overshadows insight</a> -
|
||
<strong><em>Journal of Experimental Psychology</em></strong>, 1993. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13773440938721955384&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://hk1lib.org/book/1244721/20ddc5">Deep Learning:
|
||
How the Mind Overrides Experience</a> - <strong><em>Cambridge University
|
||
Press</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=231021877034210140">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="the-aha-moment">The Aha! Moment</h4>
|
||
<ul>
|
||
<li><p><a href="https://en.wikipedia.org/wiki/Eureka_effect">Eureka
|
||
Effect</a> - <strong><em>Wikipedia</em></strong>. Wikipedia on Eureka
|
||
effect (a.k.a. Aha! moment, insight, and epiphany), the common human
|
||
experience of suddenly understanding a previously incomprehensible
|
||
problem or concept.</p></li>
|
||
<li><p><a href="https://en.wikipedia.org/wiki/Insight">Insight</a> -
|
||
<strong><em>Wikipedia</em></strong>. Wikipedia on insight.</p></li>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Epiphany_(feeling)">Epiphany</a> -
|
||
<strong><em>Wikipedia</em></strong>. Wikipedia on epiphany, the
|
||
“feeling” when the Aha! moment comes.</p></li>
|
||
<li><p><a href="https://escholarship.org/uc/item/54x8v354">A
|
||
computational model of scientific insight</a> - <strong><em>The nature
|
||
of creativity: Contemporary psychological perspectives</em></strong>,
|
||
1988. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13633357571064621019&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A computational account on insights for scientific
|
||
discovery.</p></li>
|
||
<li><p><a
|
||
href="https://www.researchgate.net/profile/Thomas-Ormerod/publication/8909475_What_Makes_an_Insight_Problem_The_Roles_of_Heuristics_Goal_Conception_and_Solution_Recoding_in_Knowledge-Lean_Problems/links/00b7d5159f3c057eb5000000/What-Makes-an-Insight-Problem-The-Roles-of-Heuristics-Goal-Conception-and-Solution-Recoding-in-Knowledge-Lean-Problems.pdf">What
|
||
Makes an Insight Problem? The Roles of Heuristics, Goal Conception, and
|
||
Solution Recoding in Knowledge-Lean Problems</a> - <strong><em>Journal
|
||
of Experimental Psychology</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17529631069707671285&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://psycnet.apa.org/record/2003-10949-002">APA</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.hf.uni-koeln.de/data/fgpsych/File/Haider/Knoblich_etal_1999.pdf">Constraint
|
||
relaxation and chunk decomposition in insight problem solving</a> -
|
||
<strong><em>Journal of Experimental Psychology</em></strong>, 1999. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8057214169831054227&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://psycnet.apa.org/record/1999-01477-011">APA</a>].</p></li>
|
||
<li><p><a
|
||
href="https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=818fec7c896ea3716eeb637da095293e9e6d1806">Dynamics
|
||
and constraints in insight problem solving</a> - <strong><em>Journal of
|
||
Experimental Psychology</em></strong>, 2002. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12067671710370549516&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://psycnet.apa.org/record/2002-01361-014">APA</a>].</p></li>
|
||
<li><p><a
|
||
href="https://bpb-us-e1.wpmucdn.com/sites.northwestern.edu/dist/a/699/files/2015/11/Salvi_etal_Insight-is-right_TR2016-2n3ns9l.pdf">Insight
|
||
solutions are correct more often than analytic solutions</a> -
|
||
<strong><em>Thinking & Reasoning</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=883561570778414219&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1094&context=jps">Human
|
||
Performance on Insight Problem Solving: A Review</a> - <strong><em>The
|
||
Journal of Problem Solving</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15913242870565808883&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.frontiersin.org/articles/10.3389/fpsyg.2016.01424/full">Insight
|
||
Is Not in the Problem: Investigating Insight in Problem Solving across
|
||
Task Types</a> - <strong><em>Frontiers in Psychology</em></strong>,
|
||
2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4564128114316001308&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.researchgate.net/profile/Trina-Kershaw/publication/8909474_Multiple_Causes_of_Difficulty_in_Insight_The_Case_of_the_Nine-Dot_Problem/links/55dca27e08aeb38e8a8d23b6/Multiple-Causes-of-Difficulty-in-Insight-The-Case-of-the-Nine-Dot-Problem.pdf">Multiple
|
||
Causes of Difficulty in Insight: The Case of the Nine-Dot Problem</a> -
|
||
<strong><em>Journal of Experimental Psychology</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15600199808825346018&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://psycnet.apa.org/record/2003-10949-001">APA</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.researchgate.net/profile/Gary-Jones-14/publication/23152585_Investigating_the_Effect_of_Mental_Set_on_Insight_Problem_Solving/links/0fcfd50abb767b1102000000/Investigating-the-Effect-of-Mental-Set-on-Insight-Problem-Solving.pdf">Investigating
|
||
the effect of Mental Set on Insight Problem Solving</a> -
|
||
<strong><em>Experimental Psychology</em></strong>, 2008. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11054712671934144981&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="rationality">Rationality</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/bounded-rationality/">Bounded
|
||
Rationality</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on Bounded Rationality, an elementary
|
||
hypothesis of human intelligence in psychology and ecology.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/rationality-instrumental/">Instrumental
|
||
Rationality</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on Instrumental Rationality, a dabate
|
||
on whether an agent’s decision is made intentionally or out of rational
|
||
coherence.</p></li>
|
||
<li><p><a
|
||
href="http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/89AdaptiveNature.pdf">The
|
||
Adaptive Nature of Human Categorization Behavior</a> -
|
||
<strong><em>Psychological Review</em></strong>, 1991. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7349048316173616836&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper that relates cognitive resource
|
||
limitation with Bayesian rational analysis, in the case of
|
||
categorization behavior.</p></li>
|
||
<li><p><a
|
||
href="https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(03)00028-7?large_figure=true&mobileUi=0">Task
|
||
switching</a> - <strong><em>Trends in Cognitive Sciences</em></strong>,
|
||
2003. [<a
|
||
href="https://scholar.google.com/scholar?cluster=676255515965300942&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="http://psychfiles.net/experimental/Monsell_2003.pdf">Preprint</a>].
|
||
The original paper on ``switch cost’‘, where subjects’ responses are
|
||
substantially slower and, usually, more error-prone immediately after a
|
||
task switch.</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/full/10.1111/tops.12086">Computational
|
||
Rationality: Linking Mechanism and Behavior Through Bounded Utility
|
||
Maximization</a> - <strong><em>Topics in Cognitive
|
||
Science</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15813211310327194798&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Introducing the computational rationality framework for
|
||
including information-processing bounds in rational analyses, which
|
||
emphasizes the incorporation of computational mechanism into the
|
||
definition of rational action.</p></li>
|
||
<li><p><a
|
||
href="https://gershmanlab.com/pubs/GershmanHorvitzTenenbaum15.pdf">Computational
|
||
rationality: A converging paradigm for intelligence in brains, minds,
|
||
and machines</a> - <strong><em>Science</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7744057022238735461&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review on the rationality of Bayesian
|
||
computational models.</p></li>
|
||
<li><p><a
|
||
href="https://cocosci.princeton.edu/papers/lieder_resource.pdf">Resource-rational
|
||
analysis: Understanding human cognition as the optimal use of limited
|
||
computational resources</a> - <strong><em>Behavioral and Brain
|
||
Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1642626865293965288&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A resource-rational account on interpreting human
|
||
intelligence.</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/full/10.1111/tops.12142">Rational
|
||
Use of Cognitive Resources: Levels of Analysis Between the Computational
|
||
and the Algorithmic</a> - <strong><em>Topics in Cognitive
|
||
Science</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16305499937147933368&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An earlier version of the paper above.</p></li>
|
||
<li><p><a
|
||
href="https://cocosci.princeton.edu/papers/griffiths_understanding.pdf">Understanding
|
||
Human Intelligence through Human Limitations</a> - <strong><em>Trends in
|
||
Cognitive Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=6469796133334580403">All
|
||
Versions</a>]. Tom Griffiths’s review on understanding the uniqueness of
|
||
human intelligence through three aspects of human limitations.</p></li>
|
||
<li><p><a
|
||
href="https://eccl.mit.edu/s/Pelz_Foundations-of-intuitive-power-analyses-in-children-and-adults.pdf">Foundations
|
||
of intuitive power analyses in children and adults</a> -
|
||
<strong><em>Nature Human Behavior</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4370839893505978405&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Evidences support that people have some of the
|
||
foundations for ‘intuitive power analyses’, which help people use
|
||
intuitive statistical reasoning and metacognitive strategies to estimate
|
||
how much information they might need to solve different discrimination
|
||
problems.</p></li>
|
||
<li><p><a
|
||
href="https://cocosci.princeton.edu/papers/ho2022cognitive.pdf">Cognitive
|
||
Science as a Source of Forward and Inverse Models of Human Decisions for
|
||
Robotics and Control</a> - <strong><em>Annual Review of Control,
|
||
Robotics, and Autonomous Systems</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&cluster=14055765901243029337">All
|
||
Versions</a>]. The review focuses on how cognitive science can provide
|
||
forward models of human decision-making and inverse models of how humans
|
||
think about others’ decision-making. The authors highlight relevant
|
||
recent developments, including approaches that synthesize black box and
|
||
theory-driven modeling, accounts that recast heuristics and biases as
|
||
forms of bounded optimality, and models that characterize human theory
|
||
of mind and communication in decision-theoretic terms.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="cognitive-architecture">Cognitive Architecture</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/epistemology/">Epistemology</a>
|
||
- <strong><em>Plato Stanford</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661321001285">The
|
||
secret life of predictive brains: what’s spontaneous activity for?</a> -
|
||
<strong><em>Trends in Cognitive Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=719229834892860829&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A neuroscience account on brain as a generative
|
||
model.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/0004370287900506">SOAR:
|
||
An architecture for general intelligence</a> - <strong><em>Artificial
|
||
Intelligence</em></strong>, 1987. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10873259207109132615&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2013/09/Anderson91.pdf">Is
|
||
human cognition adaptive?</a> - <strong><em>Behavioral and Brain
|
||
Sciences</em></strong>, 1991. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3639936076538071052&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper introducing the adaptation perspective
|
||
of human intelligence, the theoretical basis of the ACT cognitive
|
||
architecture.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0004370205001530">Metacognition
|
||
in computation: A selected research review</a> - <strong><em>Artificial
|
||
Intelligence</em></strong>, 2005. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4240334051245008914&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0010027718301604">Basic
|
||
functional trade-offs in cognition: An integrative framework</a> -
|
||
<strong><em>Cognition</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11475742130443069967&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://doi.org/10.1126/SCIENCE.AAN8871">What is
|
||
consciousness, and could machines have it?</a> -
|
||
<strong><em>Science</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6932714857132107942&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A perspective on the two levels of consciousness in
|
||
machine intelligence.</p></li>
|
||
<li><p><a
|
||
href="https://www.worldscientific.com/doi/abs/10.1142/S2705078521500028">A
|
||
Theoretical Computer Science Perspective on Consciousness</a> -
|
||
<strong><em>Journal of Artificial Intelligence and
|
||
Consciousness</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16430561748075101972&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="science-logology">Science Logology</h3>
|
||
<h4 id="philosophy-of-science">Philosophy of Science</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www-inst.eecs.berkeley.edu/~cs298-7/fa20/readings/kuhn.pdf">The
|
||
structure of scientific revolutions</a> - <strong><em>University of
|
||
Chicago Press: Chicago</em></strong>, 1970. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8909475038284903063&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Thomas Kuhn’s original book on the emergence and the
|
||
shift of scientific paradigms.</p></li>
|
||
<li><p><a href="https://jamacoartney.net/Abend%20(2008).pdf">The Meaning
|
||
of “Theory”</a> - <strong><em>Sociological Theory</em></strong>, 2008.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=4876642889050563131&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A philosophical account on the definition of “theory” in
|
||
social science (also can be generalized to natural science).</p></li>
|
||
<li><p><a
|
||
href="https://journals.sagepub.com/doi/pdf/10.4256/mio.2013.015">The
|
||
blind men and the elephant: A metaphor to illuminate the role of
|
||
researchers and reviewers in social science</a> -
|
||
<strong><em>Methodological Innovations Online</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1654629562068006152&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/abs/10.1145/3576896">A
|
||
Computational Inflection for Scientific Discovery</a> -
|
||
<strong><em>Communications of the ACM</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1756108647531090189&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="science-of-science">Science of Science</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Metascience">Metascience</a> -
|
||
<strong><em>Wikipedia</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="http://ctbergstrom.com/publications/pdfs/2018Science.pdf">Science
|
||
of Science</a> - <strong><em>Science</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6471468823556848055&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive large-scale review on the science of
|
||
science.</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/doi/abs/10.1073/pnas.0307752101">Finding
|
||
Scientific Topics</a> - <strong><em>Proceedings of the National Academy
|
||
of Sciences</em></strong>, 2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17382767110929995134&hl=zh-CN&as_sdt=0,5">All
|
||
Versions</a>]. Thomas L. Griffiths’s analysis of scientific topics using
|
||
Bayesian model.</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/doi/10.1073/pnas.1618569114">Meta-assessment
|
||
of Bias in Science</a> - <strong><em>Proceedings of the National Academy
|
||
of Sciences</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14575889060982308028&hl=zh-CN&as_sdt=0,5">All
|
||
Verisions</a>]. An analysis of bias patterns and risk factors in
|
||
science.</p></li>
|
||
<li><p><a href="https://www.pnas.org/doi/10.1073/pnas.2021636118">Slowed
|
||
Canonical Progress in Large Fields of Science</a> -
|
||
<strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7541922918797308487&hl=zh-CN&as_sdt=0,5">All
|
||
Verisions</a>]. An analysis of why too many papers published each year
|
||
in a field can lead to stagnation rather than advance.</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/10.1145/2858036.2858283">HCI
|
||
Research as Problem-Solving</a> - <strong><em>ACM
|
||
SIGCHI’16</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3206201064123443333&as_sdt=0,5">All
|
||
Versions</a>]. This essay contributes a meta-scientific account of
|
||
human-computer interaction (HCI) research as problem-solving. We build
|
||
on the philosophy of Larry Laudan, who develops problem and solution as
|
||
the foundational concepts of science. We argue that most HCI research is
|
||
about three main types of problem: empirical, conceptual, and
|
||
constructive.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="literature-mining">Literature Mining</h4>
|
||
<ul>
|
||
<li><p><a href="https://galactica.org/static/paper.pdf">Galactica: A
|
||
Large Language Model for Science</a> - <strong><em>Meta
|
||
AI</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15782429788006956926&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A large language model trained on large-scale scientific
|
||
corpus.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2205.03512">CORWA: A
|
||
Citation-Oriented Related Work Annotation Dataset</a> -
|
||
<strong><em>NAACL’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14605899782190710454&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://aclanthology.org/2021.acl-demo.14/">ESRA:
|
||
Explainable Scientific Research Assistant</a> - <strong><em>ACL’21 Demo
|
||
Track</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4387915912582172679&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A tool for constructing and visualizing the knowledge
|
||
graph of a query keyword in literature retrieving.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="literature-visualization">Literature Visualization</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://matthewberger.github.io/papers/cite2vec.pdf">cite2vec:
|
||
Citation-Driven Document Exploration via Word Embeddings</a> -
|
||
<strong><em>IEEE Transactions on Visualization and Computer
|
||
Graphics</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6949650208780085923&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://cic.tju.edu.cn/faculty/zhangjiawan/Jiawan_Zhang_files/paper/zeyuli2020.pdf">Galex:
|
||
Exploring the evolution and intersection of disciplines</a> -
|
||
<strong><em>IEEE Transactions on Visualization and Computer
|
||
Graphics</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13313104491218225635&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="scientific-writing">Scientific Writing</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="http://library.lol/main/8036CBB1CCC448CA7E036774D810EBC0">The uses
|
||
of argument</a> - <strong><em>Cambridge University Press</em></strong>,
|
||
1958. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12052408655432810103&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Stephen Toulmin’s introduction to the Toulmin argument
|
||
pattern, which is generally consist of a claim, a justification, and a
|
||
rebuttal.</p></li>
|
||
<li><p><a href="https://www.jstor.org/stable/355200">A tagmemic approach
|
||
to paragraph analysis</a> - <strong><em>College Composition and
|
||
Communication</em></strong>, 1965. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+Tagmemic+Approach+to+Paragraph+Analysis+AL+Becker&btnG=">All
|
||
Versions</a>]. The original paper on analyzing the structure of
|
||
expository paragraphs, with the two patterns—the
|
||
Topic-Restriction-Illustration pattern and the Problem-Solution
|
||
pattern.</p></li>
|
||
<li><p><a
|
||
href="https://journals.sagepub.com/doi/abs/10.1177/0741088398015002004">The
|
||
uses and complexity of argument structures in expert and student
|
||
persuasive writing</a> - <strong><em>Written
|
||
Communication</em></strong>, 1998. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3218190258774062869&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A behaviorial study revealing the argument structures
|
||
exploited by people in argumentative writing.</p></li>
|
||
<li><p><a
|
||
href="https://pure.mpg.de/rest/items/item_3020351/component/file_3045811/content">Towards
|
||
an argument interchange format</a> - <strong><em>The Knowledge
|
||
Engineering Review</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11123720528835823517&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper introducing the Argument Interchange
|
||
Format (AIF) framework for argumentation analysis.</p></li>
|
||
<li><p><a
|
||
href="https://www.aaai.org/ocs/index.php/WS/AAAIW11/paper/viewFile/3940/4244">Speech
|
||
Acts of Argumentation: Inference Anchors and Peripheral Cues in
|
||
Dialogue</a> - <strong><em>AAAI’12</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9761955212933152906&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper introducing the Information Anchoring
|
||
Theory (IAT) as an alternate for AIF.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="science-education">Science Education</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.harvardlds.org/wp-content/uploads/2018/05/Carey-Cognitive-science-and-science-education.-American-Psychologist.pdf">Cognitive
|
||
Science and Science Education</a> - <strong><em>American
|
||
Psychologist</em></strong>, 1986. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6627805813997387166&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Susan Carey’s review on cognitive-science-based
|
||
methodologies for science education research.</p></li>
|
||
<li><p><a href="https://aclanthology.org/2023.acl-demo.2/">PersLEARN:
|
||
Research Training through the Lens of Perspective Cultivation</a> -
|
||
<strong><em>ACL’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6242389165210232890&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Research on facilitating the cultivation of scientific
|
||
perspectives, starting from a basic seed idea and progressing to a
|
||
well-articulated framework, for scientific research training in higher
|
||
education.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="democratization-of-science">Democratization of Science</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-023-06221-2">Scientific
|
||
discovery in the age of artificial intelligence</a> -
|
||
<strong><em>Nature</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A review article that examines breakthroughs over the
|
||
past decade that include self-supervised learning, which allows models
|
||
to be trained on vast amounts of unlabelled data, and geometric deep
|
||
learning, which leverages knowledge about the structure of scientific
|
||
data to enhance model accuracy and efficiency.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-023-05773-7">Human–machine
|
||
collaboration for improving semiconductor process development</a> -
|
||
<strong><em>Nature</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://www.nature.com/articles/d41586-023-01353-x">Nature
|
||
News</a>].</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/s41586-023-06555-x">A
|
||
foundation model for generalizable disease detection from retinal
|
||
images</a> - <strong><em>Nature</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-023-06185-3">Accurate
|
||
medium-range global weather forecasting with 3D neural networks</a> -
|
||
<strong><em>Nature</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/10.1126/science.adi2336">Learning
|
||
skillful medium-range global weather forecasting</a> -
|
||
<strong><em>Science</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=269756601245477923&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-023-06184-4">Skilful
|
||
nowcasting of extreme precipitation with NowcastNet</a> -
|
||
<strong><em>Nature</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2311.00176">ChipNeMo:
|
||
Domain-Adapted LLMs for Chip Design</a> - 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5962372610489019326&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41467-021-22048-9">Single-atom
|
||
alloy catalysts designed by first-principles calculations and artificial
|
||
intelligence</a> - <strong><em>Nature Communications</em></strong>,
|
||
2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/doi/abs/10.1073/pnas.2016239118">Biological
|
||
structure and function emerge from scaling unsupervised learning to 250
|
||
million protein sequences</a> - <strong><em>Proceedings of the National
|
||
Academy of Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15181490380139888639&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s00449-016-1659-9">Comparability
|
||
of automated human induced pluripotent stem cell culture: a pilot
|
||
study</a> - <strong><em>Bioprocess and Biosystems
|
||
Engineering</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/forum?id=wdGIL6lx3l">ChemCrow:
|
||
Augmenting large-language models with chemistry tools</a> -
|
||
<strong><em>NeurIPS AI for Science Workshop</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8711939262720486725&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://arxiv.org/abs/2304.05376">Preprint</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/full/10.1126/science.1250475">Reproducibility</a>
|
||
- <strong><em>Science</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=676974831306442279&hl=en&as_sdt=0,10">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/s41562-016-0021">A
|
||
manifesto for reproducible science</a> - <strong><em>Nature Human
|
||
Behavior</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/533452a">1,500
|
||
scientists lift the lid on reproducibility</a> -
|
||
<strong><em>Nature</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4204808/">How to Make
|
||
More Published Research True</a> - <strong><em>PLoS
|
||
Medicine</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10945341175996677908">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/d42473-019-00004-y">Six
|
||
factors affecting reproducibility in life science research and how to
|
||
handle them</a> - <strong><em>Nature
|
||
Advertisement</em></strong>.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/d41586-021-02428-3">Five
|
||
keys to writing a reproducible lab protocol</a> -
|
||
<strong><em>Nature</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13259206850261301938">All
|
||
Versions</a>]. This interviewing paper introduces five ways to increase
|
||
the reproducibility of experimental protocols: (i) documenting protocols
|
||
as the experiment goes; (ii) providing video illustrations in addition
|
||
to written protocols; (iii) using electronic lab notebooks (ELNs) for
|
||
managing experimental resources digitally; (iv) depositing and
|
||
documenting reagents with understanding the rationale behind every step;
|
||
and (v) exploiting online platforms to share tips, extensions, methods,
|
||
and data among researchers.</p></li>
|
||
<li><p><a
|
||
href="https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2003779">The
|
||
Experimental Design Assistant</a> - <strong><em>PLoS
|
||
Biology</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12481490526120919925">All
|
||
Versions</a>]. [<a
|
||
href="https://www.nature.com/articles/nmeth.4462">Nature Methods
|
||
Correspondence</a>]. [<a href="https://eda.nc3rs.org.uk/">EDA
|
||
Website</a>]. The EDA is a web-based tool that guides the in vivo
|
||
researcher through the experimental design and analysis process,
|
||
providing automated feedback on the proposed design and generating a
|
||
graphical summary that aids communication with colleagues, funders,
|
||
regulatory authorities, and the wider scientific community.</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/abstract/document/10059206">Optimizing
|
||
Spaced Repetition Schedule by Capturing the Dynamics of Memory</a> -
|
||
<strong><em>IEEE Transactions on Knowledge and Data
|
||
Engineering</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=949715967083833369&hl=en&as_sdt=0,10">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://aclanthology.org/2020.findings-emnlp.261/">LEGAL-BERT: The
|
||
Muppets straight out of Law School</a> -
|
||
<strong><em>EMNLP’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11254432523766039890&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Generating answers to legal questions, analyze contracts,
|
||
and summarizing legal documents, making legal knowledge more accessible
|
||
to non-experts.</p></li>
|
||
<li><p><a
|
||
href="https://academic.oup.com/bioinformatics/article/36/4/1234/5566506">BioBERT:
|
||
a pre-trained biomedical language representation model for biomedical
|
||
text mining</a> - <strong><em>Bioinformatics</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2783127196632783403&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Answering medical questions, identifying relevant
|
||
clinical trials, and diagnosing diseases based on symptoms, making
|
||
medical information more accessible to the general public.</p></li>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/abs/10.5555/3491440.3492062">Finbert: A
|
||
pre-trained financial language representation model for financial text
|
||
mining</a> - <strong><em>IJCAI’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17844713837232165872&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Predicting stock market trends, analyzing financial
|
||
documents, and generating summaries of economic news articles, helping
|
||
to disseminate financial knowledge.</p></li>
|
||
<li><p><a href="https://aclanthology.org/D19-1371/">SciBERT: A
|
||
Pretrained Language Model for Scientific Text</a> -
|
||
<strong><em>EMNLP’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7377999893003631695&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Searching and synthesizing scientific literature, aiding
|
||
researchers in hypothesis generation, and assisting with experimental
|
||
design, making scientific knowledge more accessible.</p></li>
|
||
<li><p><a
|
||
href="https://aclanthology.org/2020.findings-emnlp.139/">CodeBERT: A
|
||
Pre-Trained Model for Programming and Natural Languages</a> -
|
||
<strong><em>EMNLP’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9055786889913621082&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Completing code, generating programming documentation,
|
||
and providing technical support, making programming knowledge more
|
||
accessible to non-experts.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="laboratory-automation">Laboratory Automation</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/full/10.1126/science.aat0650">Reconfigurable
|
||
system for automated optimization of diverse chemical reactions</a> -
|
||
<strong><em>Science</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3076614068291119943">All
|
||
Versions</a>]. [<a
|
||
href="https://www.science.org/doi/pdf/10.1126/science.aat0650">Preprint</a>].
|
||
This paper describes a plug-and-play, continuous-flow chemical synthesis
|
||
system that mitigates this challenge with an integrated combination of
|
||
hardware, software, and analytics. The system software controls the
|
||
user-selected reagents and unit operations (reactors and separators),
|
||
processes reaction analytics (high-performance liquid chromatography,
|
||
mass spectrometry, vibrational spectroscopy), and conducts automated
|
||
optimizations.</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/full/10.1126/science.aav2211">Organic
|
||
synthesis in a modular robotic system driven by a chemical programming
|
||
language</a> - <strong><em>Science</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13920677955690815682&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://www.chem.gla.ac.uk/cronin/images/pubs/387-Steiner-ScienceJan19.full.pdf">Preprint</a>].
|
||
[<a
|
||
href="https://www.science.org/doi/10.1126/science.aav8816">Perspective:
|
||
Democratizing synthesis by automation</a>]. This paper develops an
|
||
autonomous compiler and robotic laboratory platform to synthesize
|
||
organic compounds on the basis of standardized methods descriptions. The
|
||
platform comprises conventional equipment such as round-bottom flasks,
|
||
separatory funnels, and a rotary evaporator to maximize its
|
||
compatibility with extant literature. The authors showcase the system
|
||
with short syntheses of three common pharmaceuticals that proceeded
|
||
comparably to manual synthesis.</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/full/10.1126/science.abc2986">A
|
||
universal system for digitization and automatic execution of the
|
||
chemical synthesis literature</a> - <strong><em>Science</em></strong>,
|
||
2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13909991218383718512&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://www.chem.gla.ac.uk/cronin/images/pubs/Mehr-ScienceOct2020.pdf">Preprint</a>].
|
||
[<a href="https://croningroup.gitlab.io/chemputer/xdl/index.html">XDL
|
||
Documentation</a>]. [<a href="https://zenodo.org/records/3955107">XDL
|
||
Schema Database</a>]. This paper reports a software platform that uses
|
||
natural language processing to translate the organic chemistry
|
||
literature directly into editable code, which in turn can be compiled to
|
||
drive automated synthesis of the compound in the laboratory.</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/full/10.1126/science.abo0058">Digitization
|
||
and validation of a chemical synthesis literature database in the
|
||
ChemPU</a> - <strong><em>Science</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17368503277308594977">All
|
||
Versions</a>]. [<a
|
||
href="https://www.researchgate.net/profile/Aamir-Khan/publication/361857872_Digitization_and_validation_of_a_chemical_synthesis_literature_database_in_the_ChemPU/links/62cd356d00d0b451104cbfe9/Digitization-and-validation-of-a-chemical-synthesis-literature-database-in-the-ChemPU.pdf">Preprint</a>].
|
||
This paper presents an automatically executable chemical reaction
|
||
database of 100 molecules representative of the range of reactions found
|
||
in contemporary organic synthesis. The chemical reaction codes or χDLs
|
||
for the reactions have been stored in a database for version control,
|
||
validation, collaboration, and data mining. Of these syntheses, more
|
||
than 50 entries from the database have been downloaded and robotically
|
||
run in seven modular chemputers with yields and purities comparable to
|
||
those achieved by an expert chemist.</p></li>
|
||
<li><p><a
|
||
href="https://pubs.acs.org/doi/full/10.1021/jacsau.1c00303">Chemputation
|
||
and the Standardization of Chemical Informatics</a> -
|
||
<strong><em>Journal of the American Chemical Society (Au)</em></strong>,
|
||
2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3884902150148113559">All
|
||
Versions</a>]. This paper describes a standard hardware (the chemical
|
||
processing programming architecture — the ChemPU) to encompass all
|
||
chemical synthesis, an approach which unifies all chemistry automation
|
||
strategies, from solid-phase peptide synthesis, to HTE flow chemistry
|
||
platforms, while at the same time establishing a publication standard so
|
||
that researchers can exchange chemical code (χDL) to ensure
|
||
reproducibility and interoperability.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41557-020-00596-9">Convergence of
|
||
multiple synthetic paradigms in a universally programmable chemical
|
||
synthesis machine</a> - <strong><em>Nature Chemistry</em></strong>,
|
||
2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18024303106901939347">All
|
||
Versions</a>]. [<a
|
||
href="https://eprints.gla.ac.uk/231947/">Preprint</a>]. This paper shows
|
||
how the Chemputer synthesis robot can be programmed to perform many
|
||
different reactions, including solid-phase peptide synthesis, iterative
|
||
cross-coupling and accessing reactive, unstable diazirines in a single,
|
||
unified system with high yields and purity.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/s41557-022-01016-w">An
|
||
autonomous portable platform for universal chemical synthesis</a> -
|
||
<strong><em>Nature Chemistry</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4484997534431409967">All
|
||
Versions</a>]. [<a
|
||
href="https://eprints.gla.ac.uk/275574/">Preprint</a>]. This paper
|
||
presents a portable suitcase-sized chemical synthesis platform
|
||
containing all the modules required for synthesis and purification. The
|
||
system uses a chemical programming language coupled to a digital reactor
|
||
generator to produce reactors and executable protocols based on
|
||
text-based literature syntheses. Simultaneously, the platform generates
|
||
a reaction pressure fingerprint, used to monitor processes within the
|
||
reactors and remotely perform a protocol quality control.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/s41467-024-45444-3">An
|
||
integrated self-optimizing programmable chemical synthesis and reaction
|
||
engine</a> - <strong><em>Nature Communications</em></strong>, 2024. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9157508627971047184">All
|
||
Versions</a>]. This paper presents a dynamically programmable system
|
||
capable of making, optimizing, and discovering new molecules which
|
||
utilizes seven sensors that continuously monitor the reaction. By
|
||
developing a dynamic programming language, the work demonstrates the
|
||
10-fold scale-up of a highly exothermic oxidation reaction, end point
|
||
detection, as well as detecting critical hardware failures.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/s41586-020-2442-2">A
|
||
mobile robotic chemist</a> - <strong><em>Nature</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf">Preprint</a>].
|
||
This work uses a mobile robot to search for improved photocatalysts for
|
||
hydrogen production from water. The robot operated autonomously over
|
||
eight days, performing 688 experiments within a ten-variable
|
||
experimental space, driven by a batched Bayesian search algorithm. This
|
||
autonomous search identified photocatalyst mixtures that were six times
|
||
more active than the initial formulations, selecting beneficial
|
||
components and deselecting negative ones.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/s41586-023-06734-w">An
|
||
autonomous laboratory for the accelerated synthesis of novel
|
||
materials</a> - <strong><em>Nature</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17944003281308189532">All
|
||
Versions</a>]. This paper introduces the A-Lab, an autonomous laboratory
|
||
for the solid-state synthesis of inorganic powders. This platform uses
|
||
computations, historical data from the literature, machine learning (ML)
|
||
and active learning to plan and interpret the outcomes of experiments
|
||
performed using robotics. Over 17 days of continuous operation, the
|
||
A-Lab realized 41 novel compounds from a set of 58 targets including a
|
||
variety of oxides and phosphates that were identified using large-scale
|
||
ab initio phase-stability data from the Materials Project and Google
|
||
DeepMind.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-023-06792-0">Autonomous
|
||
chemical research with large language models</a> -
|
||
<strong><em>Nature</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. An artificial intelligence system driven by GPT-4 that
|
||
autonomously designs, plans and performs complex experiments by
|
||
incorporating large language models empowered by tools such as internet
|
||
and documentation search, code execution and experimental
|
||
automation.</p></li>
|
||
<li><p><a href="https://www.nature.com/articles/542125a">The Internet of
|
||
Things comes to the lab</a> - <strong><em>Nature</em></strong>, 2017.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The emergence of connected instruments and equipment
|
||
promises to untether researchers from the laboratory — letting them
|
||
fine-tune experiments and analyse data remotely.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S2590238522006385">What
|
||
is a minimal working example for a self-driving laboratory?</a> -
|
||
<strong><em>Matter</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1612804023616680548">All
|
||
Versions</a>]. This paper proposes SDL-Demo: a low-cost “Hello, World!”
|
||
for self-driving laboratories that combines “Hello, World!” tasks from
|
||
electronics, physics-based simulations, and optimization. SDL-Demo is
|
||
modular and extensible, making it an ideal candidate for low-cost
|
||
teaching and prototyping of self-driving laboratory concepts.</p></li>
|
||
<li><p><a href="https://elifesciences.org/articles/77007">Robotic search
|
||
for optimal cell culture in regenerative medicine</a> -
|
||
<strong><em>eLife</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1330075145723138159&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://elifesciences.org/articles/80609">Cell Culture:
|
||
Implementing robotics and artificial intelligence</a> -
|
||
<strong><em>eLife</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10725537391648003592&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="theory-of-mind">Theory of Mind</h3>
|
||
<ul>
|
||
<li><a href="https://en.wikipedia.org/wiki/Theory_of_mind">Theory of
|
||
Mind</a> - <strong><em>Wikipedia</em></strong>. Wikipedia on Theory of
|
||
Mind (ToM), a cognitive capability that estimating others’ goal, belief,
|
||
and desire.</li>
|
||
</ul>
|
||
<!--* [Cognitive Science](https://plato.stanford.edu/entries/cognitive-science/) - ***Plato Stanford***.
|
||
|
||
* [Intentionality](https://plato.stanford.edu/entries/intentionality/) - ***Plato Stanford***.
|
||
|
||
* [The Mind/Brain Identity Theory](https://plato.stanford.edu/entries/mind-identity/) - ***Plato Stanford***.
|
||
|
||
* [Mental Representation](https://plato.stanford.edu/entries/mental-representation/) - ***Plato Stanford***.
|
||
|
||
* [Mental Imagery](https://plato.stanford.edu/entries/mental-imagery/) - ***Plato Stanford***.
|
||
|
||
* [Temporal Consciousness](https://plato.stanford.edu/entries/consciousness-temporal/) - ***Plato Stanford***.
|
||
|
||
* [The Experience and Perception of Time](https://plato.stanford.edu/entries/time-experience/) - ***Plato Stanford***.
|
||
|
||
* [Practical Reason](https://plato.stanford.edu/entries/practical-reason/) - ***Plato Stanford***.
|
||
|
||
* [Memory](https://plato.stanford.edu/entries/memory/) - ***Plato Stanford***.-->
|
||
<!-- * [The Computational Theory of Mind](https://plato.stanford.edu/entries/computational-mind/) - ***Plato Stanford***. A computational philosophy account on ToM. -->
|
||
<ul>
|
||
<li><p><a
|
||
href="http://sll.stanford.edu/docs/2016_JaraEttinger_Gweon_Schulz_Tenenbaum_TiCS.pdf">The
|
||
naïve utility calculus: Computational principles underlying commonsense
|
||
psychology</a> - <strong><em>Trends in Cognitive Sciences</em></strong>,
|
||
2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6894095575934067763&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A perspective on understanding social interactions
|
||
through the naïve utility calculus framework.</p></li>
|
||
<li><p><a
|
||
href="https://saxelab.mit.edu/sites/default/files/publications/HoSaxeCushman2022.pdf">Planning
|
||
with theory of mind</a> - <strong><em>Trends in Cognitive
|
||
Sciences</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8461125353366208047&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A perspective on understanding Theory of Mind through
|
||
planning that consists of abstract structured causal representations and
|
||
supports efficient search and selection from innumerable possible
|
||
actions.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0010027709001607">Action
|
||
Understanding as Inverse Planning</a> -
|
||
<strong><em>Cognition</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11478704181983566675&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://ars.els-cdn.com/content/image/1-s2.0-S0010027709001607-mmc1.pdf">Appendix</a>].
|
||
The original paper on Inverse Planning, a computational implementation
|
||
of ToM.</p></li>
|
||
<li><p><a
|
||
href="http://web.mit.edu/9.s915/www/classes/theoryOfMind.pdf">Bayesian
|
||
Theory of Mind: Modeling Joint Belief-Desire Attribution</a> -
|
||
<strong><em>CogSci’11</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7454981153033683025&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://dspace.mit.edu/handle/1721.1/73768">Bayesian
|
||
Theory of Mind : modeling human reasoning about beliefs, desires, goals,
|
||
and social relations</a> - <strong><em>Ph.D. Dissertation
|
||
MIT</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16053170661075048224&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Chris Baker’s Ph.D. dissertation, a comprehensive review
|
||
on Bayesian modeling of Theory of Mind.</p></li>
|
||
<li><p><a href="https://psyarxiv.com/f692k/">The Signature of All
|
||
Things: Children Infer Knowledge States from Static Images</a> -
|
||
<strong><em>CogSci’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12380982112592086477&hl=en&as_sdt=0,5&as_ylo=2017">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661316301565?via%3Dihub">Bayesian
|
||
Brains without Probabilities</a> - <strong><em>Trends in Cognitive
|
||
Sciences</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13076510377612067772&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A perspective on human probabilistic modeling without
|
||
explicit probabilistic computation.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41562-017-0064">Rational
|
||
quantitative attribution of beliefs, desires and percepts in human
|
||
mentalizing</a> - <strong><em>Nature Human Behavior</em></strong>, 2017.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.mindcoolness.com/blog/bayesian-brain-predictive-processing/">The
|
||
Bayesian Brain: An Introduction to Predictive Processing</a> -
|
||
2018.</p></li>
|
||
<li><p><a
|
||
href="http://proceedings.mlr.press/v80/rabinowitz18a/rabinowitz18a.pdf">Machine
|
||
theory of mind</a> - <strong><em>ICML’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6267278380616425333&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S2352154618302055?via%3Dihub">Theory
|
||
of mind as inverse reinforcement learning</a> - <strong><em>Current
|
||
Opinion in Behavioral Sciences</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14959443239271810913&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/full/10.1111/tops.12371">Computational
|
||
Models of Emotion Inference in Theory of Mind: A Review and Roadmap</a>
|
||
- <strong><em>Topics in Cognitive Science</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15919410726494658168&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0010028520300633">The
|
||
Naïve Utility Calculus as a unified, quantitative framework for action
|
||
understanding</a> - <strong><em>Cognitive Psychology</em></strong>,
|
||
2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10366690800692546587&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="http://www.github.com/julianje/bishop">Project</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2102.12321.pdf">AGENT: A Benchmark
|
||
for Core Psychological Reasoning</a> -
|
||
<strong><em>ICML’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9729067071974484204&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A benchmark for AI that modeling the core knowledge of
|
||
ToM.</p></li>
|
||
<li><p><a
|
||
href="https://www.annualreviews.org/doi/pdf/10.1146/annurev-psych-081420-110718">Experimental
|
||
Games and Social Decision Making</a> - <strong><em>Annual Review of
|
||
Psychology</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4713510112126264116&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review on social ToM experiment
|
||
pafadigms.</p></li>
|
||
<li><p><a
|
||
href="https://www.aaai.org/ojs/index.php/AAAI/article/view/4574">Theory
|
||
of Minds: Understanding Behavior in Groups through Inverse Planning</a>
|
||
- <strong><em>AAAI’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6755247312077985817&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Inverse Planning in multi-agent setting.</p></li>
|
||
<li><p><a
|
||
href="https://psycnet.apa.org/fulltext/2019-58384-001.pdf?auth_token=0859666184839448b848053cd7bdceb2bdf2745a">Leveraging
|
||
Facial Expressions and Contextual Information to Investigate Opaque
|
||
Representations of Emotion</a> - <strong><em>Emotion</em></strong>,
|
||
2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9634378462684744548&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://linkinghub.elsevier.com/retrieve/pii/S0010027712002235">Waiting
|
||
and weighting: Information sampling is a balance between efficiency and
|
||
error-reduction</a> - <strong><em>Cognition</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12787722822882067638&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0896627313005503?via%3Dihub">Natural
|
||
scene statistics account for the representation of scene categories in
|
||
human visual cortex</a> - <strong><em>Neuron</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14030885492052338412&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41598-018-23618-6">Using human
|
||
brain activity to guide machine learning</a> - <strong><em>Scientific
|
||
Report</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/2019-27729-001">Unit of
|
||
visual working memory: A Boolean map provides a better account than an
|
||
object does</a> - <strong><em>Journal of Experimental
|
||
Psychology</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14909735035752892020&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.pnas.org/content/117/42/26158.short">The
|
||
logic of universalization guides moral judgment</a> -
|
||
<strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13482051983012049752&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2104.02841">Learning Triadic
|
||
Belief Dynamics in Nonverbal Communication from Videos</a> -
|
||
<strong><em>CVPR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15365483338824697316&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Theory of Mind in the perception level, introduced as a
|
||
computer vision task.</p></li>
|
||
<li><p><a
|
||
href="https://dspace.mit.edu/bitstream/handle/1721.1/112291/ivc_full_preprint.pdf?sequence=1&isAllowed=y">Ten-month-old
|
||
infants infer the value of goals from the costs of actions</a> -
|
||
<strong><em>Science</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11862940312128630925&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A piece of evidence for children’s capability on
|
||
ToM.</p></li>
|
||
<li><p><a href="https://www.pnas.org/content/116/36/17747">Origins of
|
||
the concepts cause, cost, and goal in prereaching infants</a> -
|
||
<strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15973074852436355789&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://static1.squarespace.com/static/595a9f155016e1f7ead6edf1/t/61eeb3e7bbc41a23cd288f8a/1643033708945/Gandhi_etal_2021.pdf">Baby
|
||
Intuitions Benchmark (BIB): Discerning the goals, preferences, and
|
||
actions of others</a> - <strong><em>NeurIPS’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=16514364601966350574">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2011.05558.pdf">Intentonomy: a
|
||
Dataset and Study towards Human Intent Understanding</a> -
|
||
<strong><em>CVPR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5268870345003195142&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A large-scale database on human intentionally-posted
|
||
images on social media.</p></li>
|
||
<li><p><a
|
||
href="https://www.tshu.io/HeiderSimmel/CogSci20/Flatland_CogSci20.pdf">Adventures
|
||
in Flatland: Perceiving Social Interactions Under Physical Dynamics</a>
|
||
- <strong><em>CogSci’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1928005249823745390&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ojs.aaai.org/index.php/AAAI/article/view/16167">PHASE:
|
||
PHysically-grounded Abstract Social Events for Machine Social
|
||
Perception</a> - <strong><em>AAAI’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15536873427310696150&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a href="https://tshu.io/PHASE/">Project</a>].</p></li>
|
||
<li><p><a
|
||
href="https://openreview.net/forum?id=w_7JMpGZRh0">Watch-And-Help: A
|
||
Challenge for Social Perception and Human-AI Collaboration</a> -
|
||
<strong><em>ICLR’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=16340001407726295133">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="analogy">Analogy</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/metaphor/">Metaphor</a> -
|
||
<strong><em>Plato Stanford</em></strong>. A computational philosophy
|
||
account on Metaphor, a poetically or rhetorically ambitious use of
|
||
words, a figurative as opposed to literal use.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/reasoning-analogy/">Analogy and
|
||
Analogical Reasoning</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on Analogy, a comparison between two
|
||
objects, or systems of objects, that highlights respects in which they
|
||
are thought to be similar.</p></li>
|
||
<li><p><a href="https://1lib.net/book/1165963/e9aa3d">A Cognitive Theory
|
||
of Metaphor</a> - <strong><em>MIT Press</em></strong>, 1985. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=a+cognitive+theory+of+metaphor&btnG=">All
|
||
Versions</a>]. A cognitive account on Metaphor.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/0004370289900775">The
|
||
structure-mapping engine: Algorithm and examples</a> -
|
||
<strong><em>Artificial Intelligence</em></strong>, 1989. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16104901325436513899&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A computational implementation of analogy.</p></li>
|
||
<li><p><a
|
||
href="https://cogsci.ucsd.edu/~coulson/203/gentner-markman-97.pdf">Structure
|
||
mapping in analogy and similarity</a> - <strong><em>American
|
||
Psychologist</em></strong>, 1997. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3497411606978611830&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A perspective unifying analogy and similarity
|
||
judgement.</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/2022-26663-001">A theory
|
||
of relation learning and cross-domain generalization</a> -
|
||
<strong><em>Psychological Review</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8559821723107269122&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review on the perspective of treating
|
||
analogy as cross-domain generalization.</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/pnas/116/10/4176.full.pdf">Emergence
|
||
of analogy from relation learning</a> - <strong><em>Proceedings of the
|
||
National Academy of Sciences</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4877125748339538047&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Analogy feature in language models.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.mlr.press/v97/allen19a.html">Analogies
|
||
Explained: Towards Understanding Word Embeddings</a> -
|
||
<strong><em>ICML’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15445529659618849253&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Explaining the analogy capability in word
|
||
embeddings.</p></li>
|
||
<li><p><a href="https://aclanthology.org/P17-1007/">Skip-Gram − Zipf +
|
||
Uniform = Vector Additivity</a> - <strong><em>ACL’17</em></strong>,
|
||
2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11732363456979525246&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.iiia.csic.es/~enric/papers/generalize_and_blend.pdf">Generalize
|
||
and Blend: Concept Blending Based on Generalization, Analogy, and
|
||
Amalgams</a> - <strong><em>ICCC’15</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11073359237116879862&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://proceedings.mlr.press/v28/juhwang13.pdf">Analogy-preserving
|
||
Semantic Embedding for Visual Object Categorization</a> -
|
||
<strong><em>ICML’13</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9332855910734484101&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The first application of analogy to machine
|
||
learning.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2015/file/45f31d16b1058d586fc3be7207b58053-Paper.pdf">VISALOGY:
|
||
Answering Visual Analogy Questions</a> -
|
||
<strong><em>NeurIPS’15</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7665427758655324654&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/9010418">Detecting
|
||
Unseen Visual Relations Using Analogies</a> -
|
||
<strong><em>CVPR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16686853801653819556&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0004370218301863">Analogy
|
||
between concepts</a> - <strong><em>Artificial
|
||
Intelligence</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1397905953174123757&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A mathematical account on analogy.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1902.00120">Learning to Make
|
||
Analogies by Contrasting Abstract Relational Structure</a> -
|
||
<strong><em>ICLR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15521573039503233138&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://aclanthology.org/2020.figlang-1.pdf#page=140">Sky + Fire =
|
||
Sunset. Exploring Parallels between Visually Grounded Metaphors and
|
||
Image Classifiers</a> - <strong><em>ACL’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5747285277687442001&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2006.04156.pdf">Analogy as
|
||
Nonparametric Bayesian Inference over Relational Systems</a> -
|
||
<strong><em>CogSci’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1798148167130120057&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.jhu.edu/~alanlab/Pubs21/ichien2021visual.pdf">Visual
|
||
Analogy: Deep Learning Versus Compositional Models</a> -
|
||
<strong><em>CogSci’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1187822306970312749&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A human-deep-learning comparison on similarity
|
||
judgement.</p></li>
|
||
<li><p><a
|
||
href="https://escholarship.org/content/qt3j2576vv/qt3j2576vv.pdf">Preschoolers
|
||
and adults make inferences from novel metaphors</a> -
|
||
<strong><em>CogSci’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16038983545360341739&hl=en&as_sdt=0,44">All
|
||
Versions</a>]. A piece of evidence that understanding metaphors is
|
||
capable for different cognitive development phases.</p></li>
|
||
<li><p><a
|
||
href="https://pcl.sitehost.iu.edu/rgoldsto/pdfs/simdiff.pdf">Similarity
|
||
involving attributes and relations: Judgments of similarity and
|
||
difference are not inverses</a> - <strong><em>Psychological
|
||
Science</em></strong>, 1990. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13205938250772079784&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="causality">Causality</h3>
|
||
<ul>
|
||
<li><p><a href="https://en.wikipedia.org/wiki/Causality">Causality</a> -
|
||
<strong><em>Wikipedia</em></strong>. Wikipedia on causality, which is
|
||
influence by which one event, process, state, or object (a cause)
|
||
contributes to the production of another event, process, state, or
|
||
object (an effect) where the cause is partly responsible for the effect,
|
||
and the effect is partly dependent on the cause.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/causal-models/">Causal
|
||
Models</a> - <strong><em>Plato Stanford</em></strong>. A computational
|
||
philosophy account on Causal models, which are mathematical models
|
||
representing causal relationships within an individual system or
|
||
population.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/content-causal/">Causal
|
||
Theories of Mental Content</a> - <strong><em>Plato
|
||
Stanford</em></strong>. A computational philosophy account on causal
|
||
theories of mental content, which attempts to explain how thoughts can
|
||
be about things.</p></li>
|
||
<li><p><a
|
||
href="http://www.jakebowers.org/ITVExperiments/angristimbensrubin96.pdf">Identification
|
||
of Causal Effects Using Instrumental Variables</a> - <strong><em>Journal
|
||
of the American Statistical Association</em></strong>, 1996. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17166265099721941605">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.psych.uni-goettingen.de/de/cognition/publikationen-dateien-waldmann/1992_predictive_vs_diagnostic.pdf">Predictive
|
||
and Diagnostic Learning Within Causal Models: Asymmetries in Cue
|
||
Competition</a> - <strong><em>Journal of Experimental
|
||
Psychology</em></strong>, 1992. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9614241045842043939&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Experimental evidences for distincting causality and
|
||
association.</p></li>
|
||
<li><p><a
|
||
href="https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780195376746.001.0001/oxfordhb-9780195376746-e-46">Causal
|
||
Reasoning</a> - <strong><em>The Oxford Handbook of Cognitive
|
||
Psychology</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11361740093816709089&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://ftp.cs.ucla.edu/pub/stat_ser/R265.pdf">Reasoning
|
||
with cause and effect</a> - 1998. Judea Pearl’s tutorials on causal
|
||
reasoning with operations on Bayesian networks.</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/pdf/10.1145/3241036">The Seven
|
||
Tools of Causal Inference, with Reflections on Machine Learning</a> -
|
||
<strong><em>Communications of the ACM</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13296019510897277617&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Judea Pearl’s review on causal inference in probabilistic
|
||
graph models.</p></li>
|
||
<li><p><a
|
||
href="https://cardiacmr.hms.harvard.edu/files/cardiacmr/files/toward_causal_representation_learning.pdf">Toward
|
||
Causal Representation Learning</a> - <strong><em>Proceedings of the
|
||
IEEE</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15629454810797806102&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Yoshua Bengio’s review on the perspective of treating
|
||
causal inference as a representation learning problem.</p></li>
|
||
<li><p><a
|
||
href="https://cocosci.princeton.edu/tom/papers/tbci.pdf">Theory-Based
|
||
Causal Induction</a> - <strong><em>Psychological Review</em></strong>,
|
||
2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13980129728092173387&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Thomas Griffiths’ review on causal Bayesian theory
|
||
induction.</p></li>
|
||
<li><p><a
|
||
href="https://ojs.aaai.org//index.php/AAAI/article/view/5483">Theory-Based
|
||
Causal Transfer: Integrating Instance-Level Induction and Abstract-Level
|
||
Structure Learning</a> - <strong><em>AAAI’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9411622427165139667&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A computatinoal account on causal transfer.</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog2703_6">Inferring
|
||
causal networks from observations and interventions</a> -
|
||
<strong><em>Cognitive Science</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12050301037347772984&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="http://proceedings.mlr.press/v139/tavares21a.html">A
|
||
Language for Counterfactual Generative Models</a> -
|
||
<strong><em>ICML’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11606362305211066214&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://cogsci.mindmodeling.org/2015/papers/0418/paper0418.pdf">Constraints
|
||
on Hypothesis Selection in Causal Learning</a> -
|
||
<strong><em>CogSci’15</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=16920774374067505248&scipsc=&q=Constraints+on+hypothesis+selection+in+causal+learning&btnG=">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://cocolab.stanford.edu/papers/GerstenbergEtAl17_PsychScience.pdf">Eye-tracking
|
||
causality</a> - <strong><em>Psychological Science</em></strong>, 2017.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17518200401109470519">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=d0TfP8EAAAAJ&sortby=pubdate&citation_for_view=d0TfP8EAAAAJ:S16KYo8Pm5AC">What
|
||
happened? Reconstructing the past through vision and sound</a> - 2021.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=12975579257004398798">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psyarxiv.com/x57hf/">How do people generalize
|
||
causal relations over objects? A non-parametric Bayesian account</a> -
|
||
2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9078127785707706032&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.psych.uni-goettingen.de/de/cognition/publikationen-dateien-waldmann/2006_science.pdf">Causal
|
||
Reasoning in Rats</a> - <strong><em>Science</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17987039255457850949&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A piece of evidence for the capability of causal
|
||
reasoning in intelligent animals.</p></li>
|
||
<li><p><a
|
||
href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.183.4674&rep=rep1&type=pdf">Do
|
||
New Caledonian crows solve physical problems through causal
|
||
reasoning?</a> - <strong><em>Proceedings of the Royal Society B:
|
||
Biological Sciences</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18374985546068164189&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A piece of evidence for the capability of causal
|
||
reasoning in intelligent animals.</p></li>
|
||
<li><p><a href="http://fitelson.org/woodward/leslie.pdf">Do
|
||
six-month-old infants perceive causality?</a> -
|
||
<strong><em>Cognition</em></strong>, 1987. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14270905342434182186&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="commonsense">Commonsense</h3>
|
||
<h4 id="intuitive-physics">Intuitive Physics</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://github.com/lishiqianhugh/Intuitive_Physics_Reading_List">Intuitive
|
||
Physics Reading List</a> - <strong><em>GitHub</em></strong>. A reading
|
||
list on intuitive physics, maintained actively by Shiqian Li.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661317301262">Intuitive
|
||
Physics: Current Research and Controversies</a> - <strong><em>Trends in
|
||
Cognitive Sciences</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?start=0&hl=en&as_sdt=0,5&cluster=12085981794958916203">All
|
||
Versions</a>]. Hongjing Lu’s review on intuitive physics.</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/content/pnas/110/45/18327.full.pdf">Simulation
|
||
as an engine of physical scene understanding</a> -
|
||
<strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5892822406285231676&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://www.pnas.org/content/pnas/suppl/2013/10/18/1306572110.DCSupplemental/pnas.201306572SI.pdf?targetid=nameddest%3DSTXT">Appendix</a>].
|
||
The first attempt to computationally simulate intuitive
|
||
physics.</p></li>
|
||
<li><p><a
|
||
href="https://www.pnas.org/doi/pdf/10.1073/pnas.1610344113">Functional
|
||
neuroanatomy of intuitive physical inference</a> -
|
||
<strong><em>Proceedings of the National Academy of
|
||
Sciences</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1792195093536891402&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A piece of evidence for the functional part of intuitive
|
||
physics in human brain.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661317301134">Mind
|
||
Games: Game Engines as an Architecture for Intuitive Physics</a> -
|
||
<strong><em>Trends in Cognitive Sciences</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14527964477161848029&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Tomer Ullman’s review on simulation-based intuitive
|
||
physics.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/S0010028517301822">Learning
|
||
physical parameters from dynamic scenes</a> - <strong><em>Cognitive
|
||
Psychology</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5103729321433959736&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0010028521000190">Limits
|
||
on Simulation Approaches in Intuitive Physics</a> -
|
||
<strong><em>Cognitive Psychology</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=6329029167380621767">All
|
||
Versions</a>]. Ernest Davis’s perspective against intuitive physics,
|
||
that physcial reasoning is logical reasoning instead of
|
||
intuition.</p></li>
|
||
<li><p><a href="https://psyarxiv.com/y4a8x/download?format=pdf">Partial
|
||
Mental Simulation Explains Fallacies in Physical Reasoning</a> -
|
||
<strong><em>Cognitive Neuropsychology</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15541954459060383152&hl=en&as_sdt=2005">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41562-022-01394-8">Intuitive
|
||
physics learning in a deep-learning model inspired by developmental
|
||
psychology</a> - <strong><em>Nature Human Behavior</em></strong>, 2022.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A machine-learning dataset designed to evaluate
|
||
conceptual understanding of intuitive physics, adopting the
|
||
violation-of-expectation (VoE) paradigm from developmental psychology; a
|
||
deep-learning system that learns intuitive physics directly from visual
|
||
data, inspired by studies of visual cognition in children.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2019/hash/4191ef5f6c1576762869ac49281130c9-Abstract.html">PHYRE:
|
||
A New Benchmark for Physical Reasoning</a> -
|
||
<strong><em>NeurIPS’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9555658528231205655&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A benchmark for AI physical reasoning.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s42256-022-00583-4">Phy-Q as a
|
||
measure for physical reasoning intelligence</a> - <strong><em>Nature
|
||
Machine Intelligence</em></strong>, 2023. [<a
|
||
href="https://www.nature.com/articles/s42256-019-0072-x">NMI
|
||
Challenge</a>]. An interactive benchmark for AI physical
|
||
reasoning.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="ai-commonsense-reasoning">AI Commonsense Reasoning</h4>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/book/9781483207704/representations-of-commonsense-knowledge">Representations
|
||
of Commonsense Knowledge</a> - <strong><em>Morgan
|
||
Kaufmann</em></strong>, 1990. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8861902735724600978&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A classic book on commonsense knowledge.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007%2F3-540-53487-3_59">Towards
|
||
a theory of commonsense visual reasoning</a> -
|
||
<strong><em>FSTTCS</em></strong>, 1990. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13178231862265713961&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on visual commonsense.</p></li>
|
||
<li><p><a
|
||
href="http://cs.wellesley.edu/~cs125/reading/commonsenseAI.pdf">Commonsense
|
||
reasoning and commonsense knowledge in artificial intelligence</a> -
|
||
<strong><em>Communications of the ACM</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13786590180441485203&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Gary Marcus’s review on commonsense knowledge in
|
||
AI.</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8953217">From
|
||
Recognition to Cognition: Visual Commonsense Reasoning</a> -
|
||
<strong><em>CVPR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="http://visualcommonsense.com/">Project</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1911.11641.pdf">PIQA: Reasoning
|
||
about Physical Commonsense in Natural Language</a> -
|
||
<strong><em>AAAI’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10110424163152713144&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9156347">Visual
|
||
Commonsense R-CNN</a> - <strong><em>CVPR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/pdf?id=Byg1v1HKDB">Abductive
|
||
Commonsense Reasoning</a> - <strong><em>ICLR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16544200144479839958&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Abductive commonsense reasoning on large language
|
||
models.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007%2F978-3-030-58558-7_30">VisualCOMET:
|
||
Reasoning About the Dynamic Context of a Still Image</a> -
|
||
<strong><em>ECCV’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7681600847940772451&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2202.04800">The Abduction of
|
||
Sherlock Holmes: A Dataset for Visual Abductive Reasoning</a> - 2022.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=18355807581692234364">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://aclanthology.org/2020.emnlp-main.703.pdf">Experience
|
||
Grounds Language</a> - <strong><em>EMNLP’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3734668471751920487&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A perspective on the furture of computational linguistics
|
||
research—commonsense-driven and embodied language.</p></li>
|
||
<li><p><a href="https://aclanthology.org/2021.emnlp-main.162/">Broaden
|
||
the Vision: Geo-Diverse Visual Commonsense Reasoning</a> -
|
||
<strong><em>EMNLP’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12305856131717604775&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://www.charleskemp.com/papers/hanrpk_humanlikepropertyinductionisachallengeforlargelanguagemodels.pdf">Human-like
|
||
property induction is a challenge for large language models</a> -
|
||
<strong><em>CogSci’22</em></strong>, 2022.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2305.17390">SwiftSage: A
|
||
Generative Agent with Fast and Slow Thinking for Complex Interactive
|
||
Tasks</a> - <strong><em>NeurIPS’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3844178012869500706&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://swiftsage.github.io/">Project</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h4 id="commonsense-knowledgebase">Commonsense Knowledgebase</h4>
|
||
<ul>
|
||
<li><p><a href="https://www.wikihow.com/Main-Page">wikiHow</a> -
|
||
<strong><em>wikiHow.com</em></strong>. wikiHow is on website hosting
|
||
step-by-step “How-to” procedural instructions across various domains and
|
||
topics.</p></li>
|
||
<li><p><a href="https://theworldavatar.io/">The World Avatar</a> -
|
||
<strong><em>The World Avatar™</em></strong>. A large-scale dynamic
|
||
knowledge graph connecting concepts with relations to digitalize
|
||
molecules, buildings, cities, and countries.</p></li>
|
||
<li><p><a
|
||
href="https://faculty.cc.gatech.edu/~isbell/classes/reading/papers/lenat95cyc.pdf">CYC:
|
||
A Large-Scale Investment in Knowledge Infrastructure</a> -
|
||
<strong><em>Communications of the ACM</em></strong>, 1995. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6505009388871605141&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The first attempt to build large-scale commonse
|
||
knoweldgebase from human knowledge.</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1612.03975.pdf">ConceptNet 5.5: An
|
||
Open Multilingual Graph of General Knowledge</a> -
|
||
<strong><em>AAAI’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7089916805257737701&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Latest version of ConceptNet.</p></li>
|
||
<li><p><a
|
||
href="https://www.aaai.org/Library/Symposia/Spring/2002/ss02-09-011.php">The
|
||
Public Acquisition of Commonsense Knowledge</a> -
|
||
<strong><em>Proceedings of AAAI Spring Symposium on Acquiring (and
|
||
Using) Linguistic (and World) Knowledge for Information
|
||
Access</em></strong>, 2002. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12533779219524472080&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The first attempt for acquring commonsense knowlege from
|
||
humans’ activities on the internet.</p></li>
|
||
<li><p><a
|
||
href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.472.914&rep=rep1&type=pdf">Open
|
||
Mind Common Sense: Knowledge Acquisition from the General Public</a> -
|
||
<strong><em>OTM Confederated International Conferences’02</em></strong>,
|
||
2002. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11431785236825227404&hl=en&as_sdt=0,5">All
|
||
Versions</a>]..</p></li>
|
||
<li><p><a
|
||
href="http://www.aladdin.cs.cmu.edu/papers/pdfs/y2006/verbosity.pdf">Verbosity:
|
||
A Game for Collecting Common-Sense Facts</a> -
|
||
<strong><em>CHI’06</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7793704394155465847&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/fullHtml/10.1145/1378704.1378719">Designing
|
||
games with a purpose</a> - <strong><em>Communications of the
|
||
ACM</em></strong>, 2008. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18332117920150730595&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://people.mpi-inf.mpg.de/~ntandon/papers/aaai-2014-tandon.pdf">Acquiring
|
||
Comparative Commonsense Knowledge from the Web</a> -
|
||
<strong><em>AAAI’14</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16641273554706459553&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/abstract/document/9904017">Visual
|
||
Concept Programming: A Visual Analytics Approach to Injecting Human
|
||
Intelligence at Scale</a> - <strong><em>IEEE Transactions on
|
||
Visualization and Computer Graphics</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10724509334112758172&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. This paper presents Visual Concept Programming, a
|
||
first-of-its-kind visual analytics approach of using visual concepts to
|
||
program image data at scale while requiring a few human
|
||
efforts.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="inductive-logic-program-synthesis">Inductive Logic & Program
|
||
Synthesis</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logic-inductive/">Inductive
|
||
Logic</a> - <strong><em>Plato Stanford</em></strong>. A computational
|
||
philosophy account on Inductive Logic, which is a logic of evidential
|
||
support.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/modeltheory-fo/">First-order
|
||
Model Theory</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on First-order Model Theory, which is a
|
||
branch of mathematics that deals with the relationships between
|
||
descriptions in first-order languages and the structures that satisfy
|
||
these descriptions.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logic-paraconsistent/">Paraconsistent
|
||
Logic</a> - <strong><em>Plato Stanford</em></strong>. A computational
|
||
philosophy account on Paraconsistent Logic, where any logic is
|
||
paraconsistent as long as it is not explosive.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logical-consequence/">Logical
|
||
Consequence</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on Logical Consequence, which is about
|
||
the relation between premises and conclusions in valid
|
||
arguments.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logical-pluralism/">Logic
|
||
Pluralism</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on Logic Pluralism, which is the view
|
||
that there is more than one correct logic.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logic-firstorder-emergence/">The
|
||
Emergence of First-Order Logic</a> - <strong><em>Plato
|
||
Stanford</em></strong>. A computational philosophy account on the
|
||
emergence of first-order logic, mainly about first-order logic is
|
||
natural retrospect.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logic-higher-order/">Second-order
|
||
and Higher-order Logic</a> - <strong><em>Plato
|
||
Stanford</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/10/program_synthesis_now.pdf">Program
|
||
Synthesis</a> - <strong><em>Foundations and Trends in Programming
|
||
Languages</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5442933587668978421&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Sumit Gulwani’s comprehensive review on program
|
||
synthesis.</p></li>
|
||
<li><p><a
|
||
href="https://www.ijcai.org/Proceedings/83-1/Papers/109.pdf">The
|
||
Discovery of the Equator or Concept Driven Learning</a> -
|
||
<strong><em>IJCAI’83</em></strong>, 1983. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15712225225140903169&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on second-order metarules.</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007%2F3-540-44797-0_10">Towards
|
||
combining inductive logic programming with Bayesian networks</a> -
|
||
<strong><em>ILP’01</em></strong>, 2001. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2904180673047700407&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://www.doc.ic.ac.uk/~shm/Papers/metagol_gram.pdf">Meta-interpretive
|
||
learning: application to grammatical inference</a> - <strong><em>Machine
|
||
Learning</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17075313112718885592&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Stephen Muggleton’s original paper on Meta-Interpretive
|
||
Learning (MIL).</p></li>
|
||
<li><p><a
|
||
href="http://andrewcropper.com/pubs/ijcai15-metagolo.pdf">Learning
|
||
Efficient Logical Robot Strategies Involving Composable Objects</a> -
|
||
<strong><em>IJCAI’15</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5109851972354087162&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://andrewcropper.com/pubs/ijcai16-metafunc.pdf">Learning
|
||
Higher-Order Logic Programs through Abstraction and Invention</a> -
|
||
<strong><em>IJCAI’16</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10945054943203858325&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007%2F978-3-319-99960-9_3">How
|
||
Much Can Experimental Cost Be Reduced in Active Learning of Agent
|
||
Strategies?</a> - <strong><em>ILP’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8152380236842970357&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007/s10994-018-5710-8">Meta-Interpretive
|
||
Learning from noisy images</a> - <strong><em>Machine
|
||
Learning</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://andrewcropper.com/pubs/mlj18-metaopt.pdf">Learning
|
||
efficient logic programs</a> - <strong><em>Machine
|
||
Learning</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17955696870252443734&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="http://andrewcropper.com/pubs/mlj19-metaho.pdf">Learning
|
||
higher-order logic programs</a> - <strong><em>Machine
|
||
Learning</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6723896359456002413&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="http://andrewcropper.com/pubs/mlj19-reduce.pdf">Logical
|
||
reduction of metarules</a> - <strong><em>Machine Learning</em></strong>,
|
||
2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4577603126537024540&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://andrewcropper.com/pubs/ijcai19-playgol.pdf">Playgol:
|
||
Learning Programs Through Play</a> - <strong><em>IJCAI’19</em></strong>,
|
||
2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=556522464212000763&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1007%2Fs00354-019-00054-2">Machine
|
||
Discovery of Comprehensible Strategies for Simple Games Using
|
||
Meta-interpretive Learning</a> - <strong><em>New Generation
|
||
Computing</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11019349634035542991&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://andrewcropper.com/pubs/aaai20-forgetgol.pdf">Forgetting to
|
||
Learn Logic Programs</a> - <strong><em>AAAI’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13676986733133377042&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.ijcai.org/proceedings/2020/673">Turning 30:
|
||
New Ideas in Inductive Logic Programming</a> -
|
||
<strong><em>IJCAI’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17980870844719684257&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2008.07912">Inductive logic
|
||
programming at 30: a new introduction</a> - <strong><em>Journal of
|
||
Artificial Intelligence Research</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=317114056670544302&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A 30-year comprehensive review on Inductive Logic
|
||
Programming.</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2005.02259.pdf">Learning programs
|
||
by learning from failures</a> - <strong><em>Machine
|
||
Learning</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6797200487935462023&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.ijcai.org/proceedings/2020/320">Complete
|
||
Bottom-Up Predicate Invention in Meta-Interpretive Learning</a> -
|
||
<strong><em>IJCAI’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6085183078630665234&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2106.07464.pdf">Meta-Interpretive
|
||
Learning as Metarule Specialisation</a> - <strong><em>Machine
|
||
Learning</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14684315775211086859&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0004370204000591">Qualitative
|
||
choice logic</a> - <strong><em>Artificial Intelligence</em></strong>,
|
||
2004. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1586187056162326386&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.ijcai.org/Proceedings/16/Papers/278.pdf">Derivative-free
|
||
optimization of high-dimensional non-convex functions by sequential
|
||
random embeddings</a> - <strong><em>IJCAI’16</em></strong>, 2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15955040483290586781&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://londmathsoc.onlinelibrary.wiley.com/doi/abs/10.1112/S0024610704006106">Finitely
|
||
Generated Groups and First-Order Logic</a> - <strong><em>Journal of The
|
||
London Mathematical Society-second Series</em></strong>, 2005. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3457158221419711506&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2006.08381">DreamCoder: Growing
|
||
generalizable, interpretable knowledge with wake-sleep Bayesian program
|
||
learning</a> - 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3288385399148303844&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A incremental learning version of Bayesian program
|
||
learning.</p></li>
|
||
<li><p><a
|
||
href="https://vigilworkshop.github.io/static/papers-2021/25.pdf">Leveraging
|
||
Language for Abstraction and Program Search</a> -
|
||
<strong><em>ICML’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Leveraging+Language+for+Abstraction+and+Program+Search&btnG=">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2021/hash/f7e2b2b75b04175610e5a00c1e221ebb-Abstract.html">Program
|
||
Synthesis Guided Reinforcement Learning</a> -
|
||
<strong><em>NeurIPS’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17353674428642875269&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://cogtoolslab.github.io/pdf/wang_cogsci_2021a.pdf">Learning
|
||
Part-Based Abstractions for Visual Object Concepts</a> -
|
||
<strong><em>CogSci’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?lookup=0&q=Learning+Part-Based+Abstractions+for+Visual+Object+Concepts&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2108.07732">Program Synthesis with
|
||
Large Language Models</a> - 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15213050540818392833&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/abs/10.1145/3571249">Combining
|
||
Functional and Automata Synthesis to Discover Causal Reactive
|
||
Programs</a> - <strong><em>POPL’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10470162446663474225&as_sdt=0,5">All
|
||
Versions</a>]. A new algorithm that synthesizes functional reactive
|
||
programs from observation data, which iterates between a functional
|
||
synthesis step, which attempts to generate a transition function over
|
||
observed states, and an automata synthesis step, which adds any
|
||
additional latent state necessary to fully account for the
|
||
observations.</p></li>
|
||
<li><p><a
|
||
href="http://cap.csail.mit.edu/sites/default/files/research-pdfs/Synthesizing%20theories%20of%20human%20language%20with%20Bayesian%20program%20induction.pdf">Synthesizing
|
||
theories of human language with Bayesian program induction</a> -
|
||
<strong><em>Nature Communications</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8603772394100237159&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2306.12672">From Word Models to
|
||
World Models: Translating from Natural Language to the Probabilistic
|
||
Language of Thought</a> - 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13778788929096574993&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Rational meaning construction, a computational framework
|
||
for language-informed thinking that combines neural language models with
|
||
probabilistic models for rational inference. Linguistic meaning is
|
||
framed as a context-sensitive mapping from natural language into a
|
||
probabilistic language of thought (PLoT)–a general-purpose symbolic
|
||
substrate for generative world modeling.</p></li>
|
||
<li><p><a href="https://proceedings.mlr.press/v139/hong21a.html">Latent
|
||
Programmer: Discrete Latent Codes for Program Synthesis</a> -
|
||
<strong><em>ICML’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9789877360194738968&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Paper introducing the Latent Programmer, a program
|
||
synthesis method that first predicts a discrete latent code from
|
||
input/output examples, and then generates the program in the target
|
||
language.</p></li>
|
||
<li><p><a href="https://proceedings.mlr.press/v202/gao23f">PAL:
|
||
Program-aided Language Models</a> - <strong><em>ICML’23</em></strong>,
|
||
2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14898051625978777315&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Paper presenting an approach that uses the LLM to read
|
||
natural language problems and generate programs as the intermediate
|
||
reasoning steps, but offloads the solution step to a runtime such as a
|
||
Python interpreter. With PAL, decomposing the natural language problem
|
||
into runnable steps remains the only learning task for the LLM, while
|
||
solving is delegated to the interpreter.</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper_files/paper/2023/hash/cd40d0d65bfebb894ccc9ea822b47fa8-Abstract-Conference.html">Grammar
|
||
Prompting for Domain-Specific Language Generation with Large Language
|
||
Models</a> - <strong><em>NeurIPS’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11694070042468483715&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Grammar prompting, a simple approach to enable LLMs to
|
||
use external knowledge and domain-specific constraints, expressed
|
||
through a grammar in Backus–Naur Form (BNF), during in-context
|
||
learning.</p></li>
|
||
<li><p><a href="https://aclanthology.org/2023.acl-long.411/">Large
|
||
Language Models Meet NL2Code: A Survey</a> -
|
||
<strong><em>ACL’23</em></strong>, 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11868015824802341463&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a href="https://nl2code.github.io/">NL2Code
|
||
Website</a>]. A paper presenting a comprehensive survey of 27 existing
|
||
large language models for NL2Code, and also review benchmarks and
|
||
metrics, suggesting that the key factors contributing to the success of
|
||
large language models for NL2Code are “Large Size, Premium Data, Expert
|
||
Tuning”.</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/abs/10.1145/3597503.3608128">A
|
||
Large-Scale Survey on the Usability of AI Programming Assistants:
|
||
Successes and Challenges</a> - <strong><em>ICSE’24</em></strong>, 2024.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=3696356619002071917&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A survey finding that developers are most motivated to
|
||
use AI programming assistants because they help developers reduce
|
||
key-strokes, finish programming tasks quickly, and recall syntax, but
|
||
resonate less with using them to help brainstorm potential
|
||
solutions.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2308.10620">Large Language Models
|
||
for Software Engineering: A Systematic Literature Review</a> - 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10466731638053452642&as_sdt=0,5">All
|
||
Versions</a>]. A systematic literature review on LLM4SE, with a
|
||
particular focus on understanding how LLMs can be exploited to optimize
|
||
processes and outcomes.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="knowledge-representation">Knowledge Representation</h3>
|
||
<ul>
|
||
<li><p><a href="https://1lib.net/book/511192/9eab86">Handbook of
|
||
Knowledge Representation</a> - <strong><em>Elsevier</em></strong>, 2008.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=14732064619564679879&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A pragmatical handbook for all kinds of knowledge
|
||
representation modes.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logic-ontology/">Logic and
|
||
Ontology</a> - <strong><em>Plato Stanford</em></strong>. A computational
|
||
philosophy account on logic and ontology, mainly about the intersections
|
||
of logic and ontology in many significant philosophy problems.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/language-thought/">The Language
|
||
of Thought Hypothesis</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on the laugnage of though hypothesis,
|
||
which proposes that thinking occurs in a mental language.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/knowledge-analysis/">The
|
||
Analysis of Knowledge</a> - <strong><em>Plato
|
||
Stanford</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/scientific-representation/">Scientific
|
||
Representation</a> - <strong><em>Plato Stanford</em></strong>. A
|
||
computational philosophy account on scientific representation, focusing
|
||
on how scientific models represent their target systems.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/self-knowledge/">Self-Knowledge</a>
|
||
- <strong><em>Plato Stanford</em></strong>. A computational philosophy
|
||
account on self-knowledge, which standardly refers to knowledge of one’s
|
||
own mental states—that is, of what one is feeling or thinking, or what
|
||
one believes or desires.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/common-knowledge/">Common
|
||
Knowledge</a> - <strong><em>Plato Stanford</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/sense-data/">Sense-Data</a> -
|
||
<strong><em>Plato Stanford</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/supervenience/">Supervenience</a>
|
||
- <strong><em>Plato Stanford</em></strong>. A computational philosophy
|
||
account on supervenience, where a set of properties A supervenes upon
|
||
another set B just in case no two things can differ with respect to
|
||
A-properties without also differing with respect to their
|
||
B-properties.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logic-dialogical/">Dialogical
|
||
Logic</a> - <strong><em>Plato Stanford</em></strong>. A computational
|
||
philosophy account on dialogical logic, which is a dialogue-based
|
||
approach to logic and argumentation rooted in a research tradition that
|
||
goes back to dialectics in Greek Antiquity, when problems were
|
||
approached through dialogues in which opposing parties discussed a
|
||
thesis through questions and answers.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logic-temporal/">Temporal
|
||
Logic</a> - <strong><em>Plato Stanford</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Situation_calculus">Situation
|
||
Calculus</a> - <strong><em>Wikipedia</em></strong>. Wikipedia on
|
||
Situation Calculus, which is a logic formalism designed for representing
|
||
and reasoning about dynamical domains.</p></li>
|
||
<li><p><a href="https://plato.stanford.edu/entries/logic-modal/">Modal
|
||
Logic</a> - <strong><em>Plato Stanford</em></strong>. A computational
|
||
philosophy account on Modal Logic, which is the study of the deductive
|
||
behavior of the expressions ‘it is necessary that’ and ‘it is possible
|
||
that’.</p></li>
|
||
<li><p><a
|
||
href="https://plato.stanford.edu/entries/logic-epistemic/">Epistemic
|
||
Logic</a> - <strong><em>Plato Stanford</em></strong>. A computational
|
||
philosophy account on Epistemic Logic, which is a subfield of
|
||
epistemology concerned with logical approaches to knowledge, belief and
|
||
related notions.</p></li>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Epistemic_modal_logic">Epistemic
|
||
Modal Logic</a> - <strong><em>Wikipedia</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://perception.jhu.edu/files/PDFs/21_Relations/HafriFirestone_2021_SeeingRelations_TiCS.pdf">The
|
||
Perception of Relations</a> - <strong><em>Trends in Cognitive
|
||
Sciences</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12190078466818849725&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Chaz Firestone’s review on the perception of relation, in
|
||
constrast to the conventional reasoning view.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/0004370284900390">Commonsense
|
||
reasoning about causality: Deriving behavior from structure</a> -
|
||
<strong><em>Artificial Intelligence</em></strong>, 1984. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14940738362673077704">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.utexas.edu/ftp/qsim/papers/Kuipers-aij-86.pdf">Qualitative
|
||
Simulation</a> - <strong><em>Artificial Intelligence</em></strong>,
|
||
1986. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4945009733425184345&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Benjamin Kuipers’ original paper on qualitative
|
||
reasoning.</p></li>
|
||
<li><p><a
|
||
href="https://www.cs.utexas.edu/users/qr/QR-book.html">Qualitative
|
||
Reasoning: Modeling and Simulation with Incomplete Knowledge</a> -
|
||
<strong><em>MIT Press</em></strong>, 1994. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=6634684154722677465">All
|
||
Versions</a>]. Benjamin Kuipers’ comprehensive book on qualitative
|
||
reasoning.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0004370297000507">Qualitative
|
||
and quantitative simulation: bridging the gap</a> -
|
||
<strong><em>Artificial Intelligence</em></strong>, 1997. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=9033452473914228535">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.1023/B:SYNT.0000024912.56773.5e">Logics
|
||
for Epistemic Programs</a> - <strong><em>Synthese</em></strong>, 2004.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://tomgruber.org/writing/ontolingua-kaj-1993.pdf">A
|
||
Translation Approach to Portable Ontology Specifications</a> -
|
||
<strong><em>Knowledge Acquisition</em></strong>, 1993. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14668658395073605123&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://people.sabanciuniv.edu/~esraerdem/teaching/krr06/asp.pdf">Answer
|
||
Set Programming</a> - <strong><em>ICLPNR’99</em></strong>, 1999. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15267370435063454675&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on Answer Set Programming
|
||
(ASP).</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007%2F978-3-642-60085-2_16">Action
|
||
Languages, Answer Sets, and Planning</a> - <strong><em>The Logic
|
||
Programming Paradigms</em></strong>, 1999. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2045126541850245645&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://www.cs.ox.ac.uk/activities/ieg/e-library/sources/harnad90_sgproblem.pdf">The
|
||
Symbolic Grounding Problem</a> - <strong><em>Physica D: Nonlinear
|
||
Phenomena</em></strong>, 1990. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6279614024681929496&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-7687.2007.00585.x?__cf_chl_captcha_tk__=pmd_Q6xVT1AstoEUxA7xS3_10HyDVsk8W_DzWgOPho_Njnw-1635210931-0-gqNtZGzNA1CjcnBszQvl">Learning
|
||
overhypotheses with hierarchical Bayesian models</a> -
|
||
<strong><em>Developmental Science</em></strong>, 2007. [<a
|
||
href="https://scholar.google.com/scholar?cluster=18041836774924845900&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://escholarship.org/content/qt19v2r2ws/qt19v2r2ws.pdf">Learning
|
||
Causal Schemata</a> - <strong><em>CogSci’07</em></strong>, 2007, [<a
|
||
href="https://scholar.google.com/scholar?cluster=5008191267417189643&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://www.pnas.org/content/105/31/10687">The discovery
|
||
of structural form</a> - <strong><em>Proceedings of the National Academy
|
||
of Sciences</em></strong>, 2008. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10433149156915110486&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Chales Kemp’s review on theory induction.</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/full/10.1080/03640210701802071">A
|
||
Rational Analysis of Rule-Based Concept Learning</a> -
|
||
<strong><em>Cognitive Science</em></strong>, 2008. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7765061503727822620&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://escholarship.org/content/qt50r1c7qh/qt50r1c7qh.pdf">Modeling
|
||
semantic cognition as logical dimensionality reduction</a> -
|
||
<strong><em>CogSci’08</em></strong>, 2008. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17061801746839695691&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="http://www.charleskemp.com/papers/KempGT08.pdf">Theory
|
||
Acquisition and the Language of Thought</a> -
|
||
<strong><em>CogSci’08</em></strong>, 2008. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1839916602381147749&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://web.mit.edu/tomeru/www/papers/tlss2010.pdf">Theory
|
||
Acquisition as Stochastic Search</a> -
|
||
<strong><em>CogSci’10</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16324634056226561429&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="http://www.charleskemp.com/papers/kemptng09.pdf">A
|
||
probabilistic model of theory formation</a> -
|
||
<strong><em>Cognition</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7705799129887482041&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://core.ac.uk/display/78064072">Bootstrapping in a
|
||
language of thought: A formal model of numerical concept learning</a> -
|
||
<strong><em>Cognition</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13046606910781656302&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://cbmm-dev.mit.edu/sites/default/files/publications/CBMM-Memo-010.pdf">Concepts
|
||
in a Probabilistic Language of Thought</a> - <strong><em>Center for
|
||
Brains, Minds, and Machines MEMO No.010</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14593712389828476130">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://www.charleskemp.com/papers/kemp_exploringtheconceptualuniverse.pdf">Exploring
|
||
the Conceptual Universe</a> - <strong><em>Psychological
|
||
Review</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17824067813343816306&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://www.charleskemp.com/papers/kempj_ataxonomyofinductiveproblems.pdf">A
|
||
taxonomy of inductive problems</a> - <strong><em>Psychonomic Bulletin
|
||
& Review</em></strong>, 2014. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2571009743105592927&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://colala.berkeley.edu/papers/piantadosi2016logical.pdf">The
|
||
Logical Primitives of Thought: Empirical Foundations for Compositional
|
||
Cognitive Models</a> - <strong><em>Psychological Review</em></strong>,
|
||
2016. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5316027496661813145&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.12580">The
|
||
Emergence of Organizing Structure in Conceptual Representation</a> -
|
||
<strong><em>Cognitive Science</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4986316323923233074&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://cogtoolslab.github.io/pdf/wang_cogsci_2021b.pdf">Theory
|
||
Acquisition as Constraint-Based Program Synthesis</a> -
|
||
<strong><em>CogSci’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=525148607069840280&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://escholarship.org/uc/item/9j00x928">Connecting
|
||
perceptual and procedural abstractions in physical construction</a> -
|
||
<strong><em>CogSci’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Connecting+perceptual+and+procedural+abstractions+in+physical+construction&btnG=">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.biorxiv.org/content/10.1101/2021.03.19.385641v1.full.pdf">Invariant
|
||
representation of physical stability in the human brain</a> - 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17431019238600295521&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.146.4086&rep=rep1&type=pdf">Introduction
|
||
to The Fluent Calculus</a> - <strong><em>Linkoeping University
|
||
Electronic Press</em></strong>, 1998. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12069059079023496731&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0004370299000338">From
|
||
situation calculus to fluent calculus: State update axioms as a solution
|
||
to the inferential frame problem</a> - <strong><em>Artificial
|
||
Intelligence</em></strong>, 1999. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10854895617698839149&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://www.stat.ucla.edu/~sczhu/papers/Conf_2013/Learning_AoG_NeurIPS_2013.pdf">Unsupervised
|
||
Structure Learning of Stochastic And-Or Grammars</a> -
|
||
<strong><em>NeurIPS’13</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=4354984630817844670">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psyarxiv.com/ysndt">Algorithms of Adaptation in
|
||
Inductive Inference</a> - <strong><em>Cognitive
|
||
Psychology</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16222039361294164246&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/0010027795006743">A
|
||
representational analysis of numeration systems</a> -
|
||
<strong><em>Cognition</em></strong>, 1995. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=8852566070856662412">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://openaccess.thecvf.com/content/CVPR2022/html/Papadopoulos_Learning_Program_Representations_for_Food_Images_and_Cooking_Recipes_CVPR_2022_paper.html">Learning
|
||
Program Representations for Food Images and Cooking Recipes</a> -
|
||
<strong><em>CVPR’22</em></strong>, 2022. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=7690010749576063125">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2205.07455">Reasoning about
|
||
Procedures with Natural Language Processing: A Tutorial</a> - 2023. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11364086808527515615&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="cognitive-development">Cognitive Development</h3>
|
||
<ul>
|
||
<li><p><a href="https://arxiv.org/abs/1810.07528">Machine Common Sense
|
||
Concept Paper</a> - <strong><em>DARPA</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1603121108181262769&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. DARPA’s perspective on integrating core knowledge from
|
||
development psychology into machine intelligence systems.</p></li>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Cognitive_development">Cognitive
|
||
Development</a> - <strong><em>Wikipedia</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Cognitive+Development%3A+an+information+processing+approach&btnG=">Cognitive
|
||
development: An information processing approach</a> -
|
||
<strong><em>B.Blackwell</em></strong>, 1991. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Cognitive+development%3A+An+information+processing+approach&btnG=">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://psycnet.apa.org/record/2012-12791-001">Reconstructing
|
||
constructivism: Causal models, Bayesian learning mechanisms, and the
|
||
theory theory</a> - <strong><em>Psychological Bulletin</em></strong>,
|
||
2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11218217347365817167&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Alison Gopnik’s review on the constructivism idea of
|
||
developmental research.</p></li>
|
||
<li><p><a
|
||
href="https://doi.apa.org/doiLanding?doi=10.1037/rev0000153">Towards a
|
||
rational constructivist theory of cognitive development</a> -
|
||
<strong><em>Psychological Review</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3294824172745724080&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Fei Xu’s review extending Gopnik’s view of
|
||
constructivism, with the rationality as constraint.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S1364661312001301">The
|
||
origins of inquiry: inductive inference and exploration in early
|
||
childhood</a> - <strong><em>Trends in Cognitive Sciences</em></strong>,
|
||
2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5189329081728071335&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Laura Schulz’s review on children’s exploratory
|
||
play.</p></li>
|
||
<li><p><a
|
||
href="https://www.annualreviews.org/doi/abs/10.1146/annurev-devpsych-070120-014806">Play,
|
||
Curiosity, and Cognition</a> - <strong><em>Annual Review of
|
||
Developmental Psychology</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10278208468154249192&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>]. Laura Schulz’s review on children’s exploratory play,
|
||
which proposes a new perspective on exploratory play to explain the
|
||
emergence of irrational behaviors in play.</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/1981-32566-001">From
|
||
exploration to play: A cross-sectional study of infant free play
|
||
behavior</a> - <strong><em>Developmental Psychology</em></strong>, 1981.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=15547331535034599545&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://srcd.onlinelibrary.wiley.com/doi/abs/10.1111/1467-8624.00224">Detecting
|
||
Blickets: How Young Children Use Information about Novel Causal Powers
|
||
in Categorization and Induction</a> - <strong><em>Children
|
||
Development</em></strong>, 2003. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9049737233568227380&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://eccl.scripts.mit.edu/papers/bonawitzandschulzseriousfun.pdf">Serious
|
||
fun: Preschoolers engage in more exploratory play when evidence is
|
||
confounded</a> - <strong><em>Developmental Psychology</em></strong>,
|
||
2007. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3033619407322882147&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://stahla.pages.tcnj.edu/files/2015/08/Stahl_Feigenson_Science_2015.pdf">Observing
|
||
the unexpected enhances infants’ learning and exploration</a> -
|
||
<strong><em>Science</em></strong>, 2015. [<a
|
||
href="https://scholar.google.com/scholar?start=10&hl=en&as_sdt=0,5&cluster=9247917261616759689">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/record/2008-12114-008">Word,
|
||
thought, and deed: the role of object categories in children’s inductive
|
||
inferences and exploratory play</a> - <strong><em>Developmental
|
||
Psychology</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13947689064550390312&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0010027711000916">Where
|
||
science starts: Spontaneous experiments in preschoolers’ exploratory
|
||
play</a> - <strong><em>Cognition</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=16321989770180281706">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://alisongopnik.com/Papers_Alison/Scientific%20Thinking%20in%20young%20Children.pdf">Scientific
|
||
thinking in young children: Theoretical advances, empirical research,
|
||
and policy implications</a> - <strong><em>Science</em></strong>, 2012.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=9103846738385460508&hl=en&as_sdt=2005">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://eccl.scripts.mit.edu/papers/Finding%20New%20Facts_%20Thinking%20New%20Thoughts.pdf">Finding
|
||
New Facts; Thinking New Thoughts</a> - <strong><em>Advances in Child
|
||
Development and Behavior</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Finding+new+facts%3B+thinking+new+thoughts&btnG=">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0885201412000445">Theory
|
||
learning as stochastic search in the language of thought</a> -
|
||
<strong><em>Cognitive Development</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8036476579458645432&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.science.org/doi/abs/10.1126/science.aan2317">Infants
|
||
make more attempts to achieve a goal when they see adults persist</a> -
|
||
<strong><em>Science</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2617011825272996810&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://cognitivesciencesociety.org/cogsci20/papers/0716/0716.pdf">Knowing
|
||
when to quit: Children consider access to solutions when deciding
|
||
whether to persist</a> - <strong><em>CogSci’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15997297570269958414&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psyarxiv.com/aq3rp/">Bayesian Models of
|
||
Conceptual Development: Learning as Building Models of the World</a> -
|
||
<strong><em>Annual Review of Developmental Psychology</em></strong>,
|
||
2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=646614032563248495&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.12765">Sticking
|
||
to the Evidence? A Behavioral and Computational Case Study of
|
||
Micro-Theory Change in the Domain of Magnetism</a> -
|
||
<strong><em>Cognitive Science</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4409900195679222965&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://junyichu.mit.edu/sites/default/files/documents/2018-05-14%20CogSci%20Final.pdf">Cognitive
|
||
pragmatism: Children flexibly choose between facts and conjectures</a> -
|
||
<strong><em>CogSci’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6978944437676543728&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psyarxiv.com/9yra2/">Exploratory play, rational
|
||
action, and efficient search</a> - <strong><em>CogSci’20</em></strong>,
|
||
2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17529638197045429028&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://srcd.onlinelibrary.wiley.com/doi/full/10.1111/cdev.13647?saml_referrer">Children
|
||
selectively endorse speculative conjectures</a> - <strong><em>Child
|
||
Development</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5672344544260882286&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://psycnet.apa.org/buy/2017-12497-003">Learning
|
||
higher-order generalizations through free play: Evidence from 2- and
|
||
3-year-old children</a> - <strong><em>Developmental
|
||
Psychology</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4386474921214936914&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/S1364661320301741">The
|
||
Child as Hacker</a> - <strong><em>Trends in Cognitive
|
||
Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=13128656954836679743&hl=en&as_sdt=2005&sciodt=0,5&as_ylo=2017">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0502">Childhood
|
||
as a solution to explore–exploit tensions</a> -
|
||
<strong><em>Philosophical Transactions of the Royal Society B:
|
||
Biological Sciences</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11960188575664977017&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41467-021-23431-2">Children’s
|
||
exploratory play tracks the discriminability of hypotheses</a> -
|
||
<strong><em>Nature Communications</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://srcd.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-8624.2010.01499.x?saml_referrer">A
|
||
Developmental Perspective on Executive Function</a> - <strong><em>Child
|
||
Development</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11347590808138984649&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://journals.sagepub.com/doi/pdf/10.1177/1745691620904771">Rethinking
|
||
Executive Function and Its Development</a> - <strong><em>Psychological
|
||
Science</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16570230278367237499&hl=en&as_sdt=2005&sciodt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.harvardlds.org/wp-content/uploads/2017/01/Perception-of-partly-occluded-objects-in-infancy-1.pdf">Perception
|
||
of partly occluded objects in infancy</a> - <strong><em>Cognitive
|
||
Psychology</em></strong>, 1983. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4182861116190610992&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/article/10.3758/s13428-012-0210-4">Age-of-acquisition
|
||
ratings for 30,000 English words</a> - <strong><em>Behavior Research
|
||
Methods</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a href="http://crr.ugent.be/archives/806">Project</a>].
|
||
A database for age-of-acquisition ratings for over 30k English
|
||
words.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="learning-in-the-open-world">Learning in the Open World</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/S002224961730010X">Online
|
||
learning of symbolic concepts</a> - <strong><em>Journal of Mathematical
|
||
Psychology</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?start=20&hl=en&as_sdt=2005&sciodt=0,5&cites=8036476579458645432&scipsc=">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8413121">Zero-Shot
|
||
Learning—A Comprehensive Evaluation of the Good, the Bad and the
|
||
Ugly</a> - <strong><em>IEEE Transactions on Pattern Analysis and Machine
|
||
Intelligence</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review on zero-shot learning.</p></li>
|
||
<li><p><a
|
||
href="https://www.4paradigm.com/upload/file/20210427/20210427225045_12063.pdf">Generalizing
|
||
from a few examples: A survey on few-shot learning</a> - <strong><em>ACM
|
||
Computing Survey</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7932202448069313464&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/7298799">Towards
|
||
Open World Recognition</a> - <strong><em>CVPR’15</em></strong>, 2015.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=856704237994181529&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The first paper introducing the problem of open-world
|
||
recognition.</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780542">Towards
|
||
Open Set Deep Networks</a> - <strong><em>CVPR’16</em></strong>, 2016.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2007.02519.pdf">In the Wild: From
|
||
ML Models to Pragmatic ML Systems</a> -
|
||
<strong><em>ICLR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15243890330014986346&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. A comprehensive review on incremental machine
|
||
learning.</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2002.04108.pdf">Adversarial
|
||
Filters of Dataset Biases</a> - <strong><em>ICML’20</em></strong>, 2020.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=11617966867048191189&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2009.01797.pdf">A Wholistic View
|
||
of Continual Learning with Deep Neural Networks: Forgotten Lessons and
|
||
the Bridge to Active and Open World Learning</a> - 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2640432662088551010&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2011.12216.pdf">Energy-Based
|
||
Models for Continual Learning</a> -
|
||
<strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7094884707139778576&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://energy-based-model.github.io/Energy-Based-Models-for-Continual-Learning/">Project</a>].</p></li>
|
||
<li><p><a
|
||
href="https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhou_Learning_to_Learn_Image_Classifiers_With_Visual_Analogy_CVPR_2019_paper.pdf">Learning
|
||
to Learn Image Classifiers with Visual Analogy</a> -
|
||
<strong><em>CVPR’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6285495755337309034&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/1804.04340v2.pdf">Zero-Shot Object
|
||
Detection</a> - <strong><em>ECCV’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2027060030559987993&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2103.02603v1.pdf">Towards Open
|
||
World Object Detection</a> - <strong><em>CVPR’21</em></strong>, 2021.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=9715328489246217151&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. [<a
|
||
href="https://github.com/JosephKJ/OWOD">Project</a>].</p></li>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/pdf/10.1145/3123266.3123323">Learning to
|
||
Recognise Unseen Classes by A Few Similes</a> -
|
||
<strong><em>MM’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?q=related:FZZr2BK0U6YJ:scholar.google.com/&scioq=Learning+to+Recognise+Unseen+Classes+by+A+Few+Similes&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.kr.org/2020/87/kr2020-0087-chen-et-al.pdf">Ontology-guided
|
||
Semantic Composition for Zero-Shot Learning</a> -
|
||
<strong><em>KR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1825132732653262003&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/pdf/2102.07339.pdf">OntoZSL:
|
||
Ontology-enhanced Zero-shot Learning</a> -
|
||
<strong><em>WWW’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=1042573079110416209&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2103.00070">Knowledge-aware
|
||
Zero-Shot Learning: Survey and Perspective</a> -
|
||
<strong><em>IJCAI’21</em></strong> 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2596179801089642923&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/8099612">From Red
|
||
Wine to Red Tomato: Composition with Context</a> -
|
||
<strong><em>CVPR’17</em></strong>, 2017. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6959320578989247472&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://link.springer.com/chapter/10.1007%2F978-3-030-01246-5_11">Attributes
|
||
as Operators: Factorizing Unseen Attribute-Object Compositions</a> -
|
||
<strong><em>ECCV’18</em></strong>, 2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11627198158637727139&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/9010671">Learning
|
||
Compositional Representations for Few-Shot Recognition</a> -
|
||
<strong><em>CVPR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7363445845219257348&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/9156505">Symmetry
|
||
and Group in Attribute-Object Compositions</a> -
|
||
<strong><em>CVPR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16870815556752021056&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2020/file/1010cedf85f6a7e24b087e63235dc12e-Paper.pdf">A
|
||
causal view of compositional zero-shot recognition</a> -
|
||
<strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2543173389101020482&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/10.1145/3394171.3413849">Compositional
|
||
Few-Shot Recognition with Primitive Discovery and Enhancing</a> -
|
||
<strong><em>MM’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15817839338790433509&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/9156655">Learning
|
||
Unseen Concepts via Hierarchical Decomposition and Composition</a> -
|
||
<strong><em>CVPR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14161656227038242300&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="learning-with-cognitive-plausibility">Learning with Cognitive
|
||
Plausibility</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://en.wikipedia.org/wiki/Accuracy_and_precision">Accuracy and
|
||
Precision</a> - <strong><em>Wikipedia</em></strong>. Wikipedia on the
|
||
distinctions and the trade-off between accuracy and precision.</p></li>
|
||
<li><p><a
|
||
href="https://www.annualreviews.org/doi/abs/10.1146/annurev.ps.40.020189.003131">Cognitive
|
||
Science: Definition, Status, and Questions</a> - <strong><em>Annual
|
||
Review of Psychology</em></strong>, 1989. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8549671583307260475&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="http://people.csail.mit.edu/torralba/courses/6.870/papers/Biederman_RBC_1987.pdf">Recognition-by-Components:
|
||
A Theory of Human Image Understanding</a> - <strong><em>Psychological
|
||
Review</em></strong>, 1987. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16522931798979362446&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on the recognition-by-components
|
||
theory.</p></li>
|
||
<li><p><a
|
||
href="https://www.nature.com/articles/s41586-019-1138-y">Machine
|
||
Behaviour</a> - <strong><em>Nature</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://yzhu.io/publication/dark2020engineering/paper.pdf">Dark,
|
||
Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common
|
||
Sense</a> - <strong><em>Engineering</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12292747257300299161&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Yixin Zhu and Song-Chun Zhu’s review on visual
|
||
commonsense.</p></li>
|
||
<li><p><a
|
||
href="https://cims.nyu.edu/~brenden/papers/OrhanEtAl2020NeurIPS.pdf">Self-supervised
|
||
Learning Through the eyes of a Child</a> -
|
||
<strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5608715260418451299&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. Concept learning through near-natural co-occurrence
|
||
frequency estimation.</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/1910.01442">CLEVRER: CoLlision
|
||
Events for Video REpresentation and Reasoning</a> -
|
||
<strong><em>ICLR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=4352064462350202338&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2020/hash/bf15e9bbff22c7719020f9df4badc20a-Abstract.html">BONGARD-LOGO:
|
||
A New Benchmark for Human-Level Concept Learning and Reasoning</a> -
|
||
<strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9164011458889391917&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://dl.acm.org/doi/10.1145/1143844.1143874">The
|
||
relationship between Precision-Recall and ROC curves</a> -
|
||
<strong><em>ICML’06</em></strong>, 2006. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10708180947310062390&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="http://export.arxiv.org/pdf/2009.08092">Distributional
|
||
Generalization: A New Kind of Generalization</a> - 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6190621467796247477&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/abs/pii/0010027793900584">Learning
|
||
and development in networks: The importance of starting small.</a> -
|
||
<strong><em>Cognition</em></strong>, 1993. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5133345254007462915&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on the idea of curriculum
|
||
learning.</p></li>
|
||
<li><p><a
|
||
href="https://www.sciencedirect.com/science/article/pii/S0010027799000311">Language
|
||
acquisition in the absence of explicit negative evidence: how important
|
||
is starting small?</a> - <strong><em>Cognition</em></strong>, 1999. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11813578367725362166&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://dl.acm.org/doi/pdf/10.1145/1553374.1553380">Curriculum
|
||
Learning</a> - <strong><em>ICML’09</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8740915934335425405&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper applying the idea of curriculum
|
||
learning to machine learning.</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/6126279">Parsing
|
||
video events with goal inference and intent prediction</a> -
|
||
<strong><em>ICCV’11</em></strong>, 2011. [<a
|
||
href="https://scholar.google.com/scholar?cluster=5979196784405021658&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://ieeexplore.ieee.org/document/6751387">Inferring
|
||
“Dark Matter” and “Dark Energy” from Videos</a> -
|
||
<strong><em>ICCV’13</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=3467068307444498624&hl=en&as_sdt=0,5">All
|
||
Versions</a>]. The original paper on latent state discovery from
|
||
videos.</p></li>
|
||
<li><p><a
|
||
href="https://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_Explainable_and_Explicit_Visual_Reasoning_Over_Scene_Graphs_CVPR_2019_paper.pdf">Explainable
|
||
and Explicit Visual Reasoning over Scene Graphs</a> -
|
||
<strong><em>CVPR’19</em></strong>, 2019. [<a
|
||
href="https://scholar.google.com/scholar?cluster=8517395712319798436&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2021/hash/4c26774d852f62440fc746ea4cdd57f6-Abstract.html">Attention
|
||
over Learned Object Embeddings Enables Complex Visual Reasoning</a> -
|
||
<strong><em>NeurIPS’21</em></strong>, 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=127829313460149801&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://papers.NeurIPS.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf">Distributed
|
||
Representations of Words and Phrases and their Compositionality</a> -
|
||
<strong><em>NeurIPS’13</em></strong>, 2013. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2410615501856807729&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9197172">Motion
|
||
Reasoning for Goal-Based Imitation Learning</a> -
|
||
<strong><em>ICRA’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7519230802512388210&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Ji_Action_Genome_Actions_As_Compositions_of_Spatio-Temporal_Scene_Graphs_CVPR_2020_paper.pdf">Action
|
||
Genome: Actions as Compositions of Spatio-temporal Scene Graphs</a> -
|
||
<strong><em>CVPR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=388714326304810525&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://proceedings.neurips.cc/paper/2020/file/64dcf3c521a00dbb4d2a10a27a95a9d8-Paper.pdf">Refactoring
|
||
Policy for Compositional Generalizability using Self-Supervised Object
|
||
Proposals</a> - <strong><em>NeurIPS’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2255457416066730255&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Materzynska_Something-Else_Compositional_Action_Recognition_With_Spatial-Temporal_Interaction_Networks_CVPR_2020_paper.pdf">Something-Else:
|
||
Compositional Action Recognition with Spatial-Temporal Interaction
|
||
Networks</a> - <strong><em>CVPR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=17469863154797360929&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Putting_Visual_Object_Recognition_in_Context_CVPR_2020_paper.pdf">Putting
|
||
visual object recognition in context</a> -
|
||
<strong><em>CVPR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6207193649298787857&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2106.13884">Multimodal Few-Shot
|
||
Learning with Frozen Language Models</a> - 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=16154696122208258147&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5206772">Describing
|
||
Objects by their Attributes</a> - <strong><em>CVPR’09</em></strong>,
|
||
2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://arxiv.org/abs/2108.07783">Panoramic Learning
|
||
with A Standardized Machine Learning Formalism</a> - 2021. [<a
|
||
href="https://scholar.google.com/scholar?cluster=14222434793711614257&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://psycnet.apa.org/record/1996-10319-001">Graininess of
|
||
judgment under uncertainty: An accuracy-informativeness trade-off</a> -
|
||
<strong><em>Journal of Experimental Psychology</em></strong>, 1995. [<a
|
||
href="https://scholar.google.com/scholar?cluster=15366302654259490472&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://openreview.net/forum?id=GFsU8a0sGB">Federated
|
||
Learning via Posterior Averaging: A New Perspective and Practical
|
||
Algorithms</a> - <strong><em>ICLR’20</em></strong>, 2020. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2486025806014234529&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://www.biorxiv.org/content/10.1101/2022.01.29.478330v2.abstract">Interplay
|
||
between rule learning and rule switching in a perceptual categorization
|
||
task</a> - 2022. [<a
|
||
href="https://scholar.google.com/scholar?cluster=7461559646167397406&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<!--
|
||
### Tasks & Environments
|
||
|
||
#### Dataset Aggregation
|
||
* [A Dataset and Architecture for Visual Reasoning with a Working Memory](https://link.springer.com/chapter/10.1007%2F978-3-030-01249-6_44) - ***ECCV'18***, 2018. [[Project](https://github.com/google/cog)].
|
||
* [PHYRE: A New Benchmark for Physical Reasoning](https://research.fb.com/wp-content/uploads/2019/08/PHYRE-A-New-Benchmark-for-Physical-Reasoning-v4.pdf) - ***NeurIPS'19***, 2019.
|
||
* [CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning](https://openreview.net/forum?id=HJgzt2VKPB) - ***ICLR'20***, 2020. [[Project](https://rohitgirdhar.github.io/CATER/)].
|
||
* [CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning](https://arxiv.org/abs/2010.04296), 2020.
|
||
|
||
#### Embodied AI Environment
|
||
* [ThreeDWorld](http://www.threedworld.org/) - ***MIT-IBM***. [[Paper](https://arxiv.org/abs/2007.04954)].
|
||
* [Rearrangement: A Challenge for Embodied AI](https://arxiv.org/pdf/2011.01975.pdf), 2020.
|
||
* [iGibson](http://svl.stanford.edu/igibson/) - ***Stanford***. [[Paper](https://ieeexplore.ieee.org/document/8954627)].
|
||
* [AI2-THOR](https://ai2thor.allenai.org/ithor) - ***Allen Institute***. [[Paper](https://arxiv.org/abs/1712.05474)].
|
||
* [Robo-THOR](https://ai2thor.allenai.org/robothor) - ***Allen Institute***. [[Paper](https://arxiv.org/abs/2004.06799)].
|
||
* [Manipula-THOR](https://ai2thor.allenai.org/manipulathor) - ***Allen Institute***. [[Paper](https://arxiv.org/abs/2104.11213)].
|
||
* [RLBench](https://sites.google.com/view/rlbench) - ***Imperial College***. [[Paper](https://ieeexplore.ieee.org/document/9001253)].
|
||
|
||
#### First-Person Vision
|
||
* [First-Person Vision](https://ieeexplore.ieee.org/document/6232429) - ***Proceedings of the IEEE***, 2012.
|
||
* [The Evolution of First Person Vision Methods: A Survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7055926) - ***Trans. CSVT***, 2015.
|
||
* [Understanding the Nature of First-Person Videos: Characterization and Classification using Low-Level Features](http://vijaychan.github.io/Publications/2014%20CVPR%20Workshop%20-%20Understanding%20the%20Nature%20of%20First-Person%20Videos.pdf) - ***CVPR'14***, 2014.
|
||
* [Pooled Motion Features for First-Person Videos](https://openaccess.thecvf.com/content_cvpr_2015/papers/Ryoo_Pooled_Motion_Features_2015_CVPR_paper.pdf) - ***CVPR'15***, 2015.
|
||
* [Actor and Observer: Joint Modeling of First and Third-Person Videos](https://openaccess.thecvf.com/content_cvpr_2018/papers/Sigurdsson_Actor_and_Observer_CVPR_2018_paper.pdf) - ***CVPR'18***, 2018.
|
||
* [Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video](https://link.springer.com/chapter/10.1007/978-3-030-58452-8_41) - ***ECCV'20***, 2020.
|
||
* [Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9088213) - ***Trans. PAMI***, 2020.
|
||
* [View-Action Representation Learning for Active First-Person Vision](https://ieeexplore.ieee.org/document/9064828) - ***Trans. CSVT***, 2021.
|
||
* [Design and Use Paradigms for Gazebo, An Open-Source Multi-Robot Simulator](https://ieeexplore.ieee.org/abstract/document/1389727) - ***IROS'04***, 2004. [[Project](http://gazebosim.org/)].
|
||
* [ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning](https://arxiv.org/pdf/1605.02097v2.pdf) - ***CIG'16***, 2016. [[Project](http://vizdoom.cs.put.edu.pl/)].
|
||
* [Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset](https://arxiv.org/abs/2011.12263), 2020.
|
||
* **Visual Experience Database** [[Project](http://visualexperiencedatabase.org/research.html)]. [[Publications](http://visualexperiencedatabase.org/publications.html)].
|
||
|
||
#### Abstract Reasoning Challenge
|
||
* [On the Measure of Intelligence](https://arxiv.org/pdf/1911.01547.pdf) - ***Google Research***, 2019.
|
||
* [Abstract Reasoning Challenge](https://www.kaggle.com/c/abstraction-and-reasoning-challenge/)
|
||
|
||
#### AI Birds Challenge
|
||
* [AI-Birds](https://aibirds.org) - ***IJCAI***.
|
||
* [Hi-Phy: A Benchmark for Hierarchical Physical Reasoning](https://openreview.net/forum?id=AcL1ORzw0Nf), 2021.
|
||
|
||
#### Minecraft
|
||
* [Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research](https://eric.ed.gov/?id=EJ1097278) - ***Journal on Eduction Technology & Society***, 2016.
|
||
|
||
##### Malmo Platform for Minecraft AI
|
||
* [The Malmo Platform for Artificial Intelligence Experimentation](https://www.microsoft.com/en-us/research/publication/malmo-platform-artificial-intelligence-experimentation/) ***IJCAI'16***, 2016.
|
||
* [[Malmo](https://github.com/Microsoft/malmo#getting-started)].
|
||
* [[Malmo-env](https://github.com/Microsoft/malmo/tree/master/MalmoEnv)].
|
||
* [[Malmo-Tutorials](https://microsoft.github.io/malmo/0.17.0/Python_Examples/Tutorial.pdf)].
|
||
* [[MineRL](https://minerl.io/)].
|
||
* [[MarLo Challenge 2018](https://github.com/crowdAI/marLo)].
|
||
|
||
##### **Artificial Intelligence**
|
||
* [Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft](https://arxiv.org/abs/2106.14876), 2021.
|
||
* [Learning to execute instructions in a Minecraft dialogue](https://www.aclweb.org/anthology/2020.acl-main.232/) - ***ACL'20***, 2020.
|
||
* [Collaborative Dialogue in Minecraft](https://www.aclweb.org/anthology/P19-1537.pdf) - ***ACL'19***, 2019.
|
||
* [Learning Skill Hierarchies from Predicate Descriptions and Self-Supervision](http://web.mit.edu/tslvr/www/papers/genplan20_camera_ready.pdf) - ***AAAI GenPlan Workshop***, 2020.
|
||
* [AMRL: Aggregated Memory for Reinforcement Learning](https://openreview.net/pdf?id=Bkl7bREtDr) - ***ICLR'20***, 2020.
|
||
* [MineRL: A Large-Scale Dataset of Minecraft Demonstrations](https://www.ijcai.org/Proceedings/2019/0339.pdf) ***IJCAI'19***, 2019. [[2020 Competition](https://arxiv.org/abs/2106.03748)].
|
||
* [Design Mining for Minecraft Architecture](http://www.cs.cornell.edu/~eland/papers/aiide2018.pdf) - ***AAAI'18***, 2018.
|
||
* [Adaptive Agents in Minecraft: A Hybrid Paradigm for Combining Domain Knowledge with Reinforcement Learning](https://link.springer.com/chapter/10.1007%2F978-3-319-71679-4_6) - ***AAMAS'17***, 2017.
|
||
* [Asynchronous Data Aggregation for Training End to End Visual Control Networks](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/fp185-monfort-1.pdf) - ***AAMAS'17***, 2017.
|
||
* [A Deep Hierarchical Approach to Lifelong Learning in Minecraft](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14630/13950) - ***AAAI'17***, 2017.
|
||
* [Modular Multitask Reinforcement Learning with Policy Sketches](http://proceedings.mlr.press/v70/andreas17a.html) - ***ICML'17***, 2017.
|
||
* [Control of memory, active perception, and action in minecraft](http://proceedings.mlr.press/v48/oh16.pdf) - ***ICML'16***, 2016.
|
||
* [Learning Behavior from Demonstration in Minecraft via Symbolic Similarity Measures](fdg2015.org/papers/fdg2015_paper_11.pdf) - ***FDG'15***, 2015.
|
||
|
||
##### **Cognitive Science**
|
||
* [How Players Speak to an Intelligent GameCharacter Using Natural Language Messages](http://todigra.org/index.php/todigra/article/view/88/139) - ***DiGRA***, 2018.
|
||
* [Minecraft as a Generative Platform for Analyzing and Practicing Spatial Reasoning](https://link.springer.com/chapter/10.1007%2F978-3-030-57983-8_22) - ***Spatial Cognition'20***, 2020.
|
||
* [Generative Design in Minecraft: Chronicle Challenge](http://computationalcreativity.net/iccc2019/papers/iccc19-lbp-7.pdf) - ***ICCC'20***, 2020.
|
||
* [Minecraft as a Platform for Project-Based Learning in AI](https://aaai.org/ojs/index.php/AAAI/article/view/7070) - ***AAAI'20***, 2020.
|
||
* [MC-Saar-Instruct: a Platform for Minecraft Instruction Giving Agents](https://www.aclweb.org/anthology/2020.sigdial-1.7.pdf) - ***SIGDial'20***, 2020.
|
||
|
||
*[Back to Top](#c)-->
|
||
<h2 id="institute-researcher">Institute & Researcher</h2>
|
||
<h3 id="mit">MIT</h3>
|
||
<ul>
|
||
<li><p><a href="https://cbmm.mit.edu/">Center for Brains, Minds and
|
||
Machines (CBMM)</a> - <strong><em>MIT</em></strong>.</p></li>
|
||
<li><p><a href="https://cocosci.mit.edu/josh">Josh Tenenbaum</a> -
|
||
<strong><em>Department of Brain and Cognitive Sciences, CSAIL,
|
||
MIT</em></strong>, <a href="https://cocosci.mit.edu/">Computational
|
||
Cognitive Science Group (CoCoSci Group)</a> -
|
||
<strong><em>MIT</em></strong>.</p></li>
|
||
<li><p><a href="https://saxelab.mit.edu/people/rebecca-saxe">Rebecca
|
||
Saxe</a> - <strong><em>Department of Brain and Cognitive Sciences,
|
||
MIT</em></strong>, <a href="https://saxelab.mit.edu/">Social Cognitive
|
||
Neuroscience Laboratory (SaxeLab)</a> -
|
||
<strong><em>MIT</em></strong>.</p></li>
|
||
<li><p><a href="https://cbmm.mit.edu/about/people/schulz">Laura
|
||
Schulz</a> - <strong><em>Department of Brain and Cognitive Sciences,
|
||
MIT</em></strong>, <a href="https://eccl.mit.edu/">Early Childhood
|
||
Cognition Lab</a> - <strong><em>MIT</em></strong>.</p></li>
|
||
<li><p><a href="https://people.csail.mit.edu/lpk/">Leslie Kaelbling</a>
|
||
- <strong><em>Department of Electrical Engineering and Computer Science,
|
||
CSAIL, MIT</em></strong>, <a href="https://lis.csail.mit.edu/">The
|
||
Learning & Intelligent Systems Group</a> -
|
||
<strong><em>MIT</em></strong>.</p></li>
|
||
<li><p><a href="https://people.csail.mit.edu/asolar/">Armando
|
||
Solar-Lezama</a> - <strong><em>Department of Electrical Engineering and
|
||
Computer Science, CSAIL, MIT</em></strong>, <a
|
||
href="http://groups.csail.mit.edu/cap/">Computer-Aided Programming
|
||
Group</a> - <strong><em>MIT</em></strong>.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="stanford">Stanford</h3>
|
||
<ul>
|
||
<li><p><a href="https://profiles.stanford.edu/fei-fei-li">Li Fei-Fei</a>
|
||
- <strong><em>Computer Science Department, Human-Centered AI Institute,
|
||
Stanford</em></strong>, <a href="https://svl.stanford.edu/">Stanford
|
||
Vision and Learning Lab</a> -
|
||
<strong><em>Stanford</em></strong>.</p></li>
|
||
<li><p><a href="https://cocolab.stanford.edu/ndg.html">Noah Goodman</a>
|
||
- <strong><em>Department of Psychology, Computer Science Department,
|
||
Stanford</em></strong>, <a
|
||
href="https://cocolab.stanford.edu/">Computation & Cognition Lab
|
||
(CoCoLab)</a> - <strong><em>Stanford</em></strong>.</p></li>
|
||
<li><p><a href="https://web.stanford.edu/~mcfrank/">Michael Frank</a> -
|
||
<strong><em>Department of Psychology, Stanford</em></strong>, <a
|
||
href="http://langcog.stanford.edu/">The Stanford Language and Cognition
|
||
Lab</a> - <strong><em>Stanford</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://cicl.stanford.edu/member/tobias_gerstenberg/">Tobias
|
||
Gerstenberg</a> - <strong><em>Department of Psychology,
|
||
Stanford</em></strong>, <a href="https://cicl.stanford.edu/">Causality
|
||
in Cognition Lab (CICL)</a> -
|
||
<strong><em>Stanford</em></strong>.</p></li>
|
||
<li><p><a href="http://ai.stanford.edu/~cbfinn/">Chelsea Finn</a> -
|
||
<strong><em>Computer Science Department, Stanford</em></strong>, <a
|
||
href="https://irislab.stanford.edu/">Intelligence through Robotic
|
||
Interaction at Scale (IRIS Group)</a> -
|
||
<strong><em>Stanford</em></strong>.</p></li>
|
||
<li><p><a href="https://comm.stanford.edu/faculty-bailenson/">Jeremy
|
||
Bailenson</a> - <strong><em>Department of Communication,
|
||
Stanford</em></strong>, <a href="https://stanfordvr.com/">Virtual Human
|
||
Interaction Lab (VHIL)</a> -
|
||
<strong><em>Stanford</em></strong>.</p></li>
|
||
<li><p><a href="https://jiajunwu.com/">Jiajun Wu</a> -
|
||
<strong><em>Computer Science Department,
|
||
Stanford</em></strong>.</p></li>
|
||
<li><p><a href="https://profiles.stanford.edu/judith-fan">Judith Fan</a>
|
||
- <strong><em>Department of Psychology, Stanford</em></strong>, <a
|
||
href="https://cogtoolslab.github.io/">Cognitive Tools Lab</a> -
|
||
<strong><em>Stanford</em></strong>.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="princeton">Princeton</h3>
|
||
<ul>
|
||
<li><p><a href="https://psych.princeton.edu/person/tania-lombrozo">Tania
|
||
Lombrozo</a> - <strong><em>Department of Psychology,
|
||
Princeton</em></strong>, <a
|
||
href="https://cognition.princeton.edu/">Concepts & Cognition Lab</a>
|
||
- <strong><em>Princeton</em></strong>.</p></li>
|
||
<li><p><a href="https://cocosci.princeton.edu/tom/index.php">Thomas
|
||
Griffiths</a> - <strong><em>Department of Psychology, Department of
|
||
Computer Science, Princeton</em></strong>, <a
|
||
href="https://cocosci.princeton.edu/index.php">Computational Cognitive
|
||
Science Lab</a> - <strong><em>Princeton</em></strong>.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="harvard">Harvard</h3>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://psychology.fas.harvard.edu/people/elizabeth-s-spelke">Elizabeth
|
||
Spelke</a> - <strong><em>Department of Psychology,
|
||
Harvard</em></strong>, <a href="https://www.harvardlds.org/">Harvard
|
||
Laboratory for Developmental Studies</a> -
|
||
<strong><em>Harvard</em></strong>.</p></li>
|
||
<li><p><a href="https://www.tomerullman.org/">Tomer Ullman</a> -
|
||
<strong><em>Department of Psychology, Harvard</em></strong>, <a
|
||
href="https://cocodev.fas.harvard.edu/">Computation, Cognition, and
|
||
Development Lab (CoCoDev)</a> -
|
||
<strong><em>Harvard</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://psychology.fas.harvard.edu/people/samuel-j-gershman">Samuel
|
||
Gershman</a> - <strong><em>Department of Psychology,
|
||
Harvard</em></strong>, <a href="https://gershmanlab.com/">Computational
|
||
Cognitive Neuroscience Lab (CCN Lab)</a> -
|
||
<strong><em>Harvard</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://psychology.fas.harvard.edu/people/fiery-cushman">Fiery
|
||
Cushman</a> - <strong><em>Department of Psychology,
|
||
Harvard</em></strong>, <a
|
||
href="https://cushmanlab.fas.harvard.edu/">Moral Psychology Research
|
||
Lab</a> - <strong><em>Harvard</em></strong>.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="ucla">UCLA</h3>
|
||
<ul>
|
||
<li><p><a href="http://vcla.stat.ucla.edu/">Center for Vision,
|
||
Cognition, Learning and Autonomy (VCLA)</a> - <strong><em>Department of
|
||
Statistics, UCLA</em></strong>.</p></li>
|
||
<li><p><a href="http://www.stat.ucla.edu/~ywu/">Ying Nian Wu</a> -
|
||
<strong><em>Department of Statistics, UCLA</em></strong>.</p></li>
|
||
<li><p><a href="http://www.stat.ucla.edu/~taogao/Taogao.html">Tao
|
||
Gao</a> - <strong><em>Department of Statistics, Department of
|
||
Psychology, UCLA</em></strong>, <a
|
||
href="http://www.stat.ucla.edu/~taogao/index.html">Visual Intelligence
|
||
Lab</a> - <strong><em>UCLA</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://www.psych.ucla.edu/faculty/page/hongjing">Hongjing Lu</a>
|
||
- <strong><em>Department of Psychology, Department of Statistics,
|
||
UCLA</em></strong>, <a href="http://cvl.psych.ucla.edu/">Computational
|
||
Vision and Learning Lab (CVL)</a> -
|
||
<strong><em>UCLA</em></strong>.</p></li>
|
||
<li><p><a href="http://web.cs.ucla.edu/~guyvdb/">Guy Van den Broeck</a>
|
||
- <strong><em>Department of Computer Science, UCLA</em></strong>, <a
|
||
href="http://starai.cs.ucla.edu/#">StarAI Lab</a> -
|
||
<strong><em>UCLA</em></strong>.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="uc-berkeley">UC Berkeley</h3>
|
||
<ul>
|
||
<li><p><a href="https://people.eecs.berkeley.edu/~anca/index.html">Anca
|
||
Dragan</a> - <strong><em>Department of Electrical Engineering and
|
||
Computer Science, UC Berkeley</em></strong>, <a
|
||
href="http://interact.berkeley.edu/">Interactive Autonomy and
|
||
Collaborative Technologies Laboratory (InterACT)</a> - <strong><em>UC
|
||
Berkeley</em></strong>.</p></li>
|
||
<li><p><a href="https://psychology.berkeley.edu/people/fei-xu">Fei
|
||
Xu</a> - <strong><em>Department of Psychology, UC
|
||
Berkeley</em></strong>, <a
|
||
href="https://babylab5.wixsite.com/bell">Berkeley Early Learning Lab (Xu
|
||
Lab)</a> - <strong><em>UC Berkeley</em></strong>.</p></li>
|
||
<li><p><a href="http://alisongopnik.com/">Alison Gopnik</a> -
|
||
<strong><em>Department of Psychology, UC Berkeley</em></strong>, <a
|
||
href="http://www.gopniklab.berkeley.edu/">Cognitive Development &
|
||
Learning Lab (Gopnik Lab)</a> - <strong><em>UC
|
||
Berkeley</em></strong>.</p></li>
|
||
<li><p><a href="http://colala.berkeley.edu/people/piantadosi/">Steve
|
||
Piantadosi</a> - <strong><em>Department of Psychology, UC
|
||
Berkeley</em></strong>, <a href="http://colala.berkeley.edu/">The
|
||
computation and language lab (colala)</a> - <strong><em>UC
|
||
Berkeley</em></strong>.</p></li>
|
||
<li><p><a href="http://www.celestekidd.com/">Celeste Kidd</a> -
|
||
<strong><em>Department of Psychology, UC Berkeley</em></strong>, <a
|
||
href="https://www.kiddlab.com/">Kidd Lab</a> - <strong><em>UC
|
||
Berkeley</em></strong>.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="bnu">BNU</h3>
|
||
<ul>
|
||
<li><a
|
||
href="https://brain.bnu.edu.cn/English/Faculty/CurrentFaculty/Bzz/a552402e529a4f27b979378abd42c10e.htm">Yanchao
|
||
Bi</a> - <strong><em>IDG/McGovern Institute for Brain Research and the
|
||
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing
|
||
Normal University (BNU)</em></strong>, <a
|
||
href="http://bilab.bnu.edu.cn/">Yanchao Bi’s Concept Lab (Bi Lab)</a> -
|
||
<strong><em>BNU</em></strong>.</li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="pku">PKU</h3>
|
||
<ul>
|
||
<li><p><a href="https://zhusongchun.net/">Song-Chun Zhu</a> -
|
||
<strong><em>School of AI and Institute for AI, Peking University
|
||
(PKU)</em></strong>.</p></li>
|
||
<li><p><a href="https://yzhu.io/">Yixin Zhu</a> - <strong><em>School of
|
||
AI and Institute for AI, Peking University (PKU)</em></strong>, <a
|
||
href="https://pku.ai/">Cognitive Reasoning Lab (CoRe Lab)</a> -
|
||
<strong><em>PKU</em></strong>.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="ucsd">UCSD</h3>
|
||
<ul>
|
||
<li><p><a href="https://pages.ucsd.edu/~ztu/">Zhuowen Tu</a> -
|
||
<strong><em>Department of Computer Science, UCSD</em></strong>, <a
|
||
href="https://pages.ucsd.edu/~ztu/Group.htm">Machine Learning,
|
||
Perception, and Cognition Lab (mlPC)</a> -
|
||
<strong><em>UCSD</em></strong>.</p></li>
|
||
<li><p><a
|
||
href="https://psychology.ucsd.edu/people/profiles/evul.html">Ed Vul</a>
|
||
- <strong><em>Department of Psychology, UCSD</em></strong>, <a
|
||
href="http://www.evullab.org/index.html">Computational Cognition Lab</a>
|
||
- <strong><em>UCSD</em></strong>.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="nyu">NYU</h3>
|
||
<ul>
|
||
<li><p><a href="https://cs.nyu.edu/~davise/">Ernest Davis</a> -
|
||
<strong><em>Department of Computer Science, Courant Institute of
|
||
Mathematical Sciences, NYU</em></strong>.</p></li>
|
||
<li><p><a href="http://garymarcus.com/index.html">Gary Marcus</a> -
|
||
<strong><em>Department of Psychology, NYU</em></strong>.</p></li>
|
||
<li><p><a href="https://cims.nyu.edu/~brenden/">Brenden Lake</a> -
|
||
<strong><em>Department of Psychology, NYU</em></strong>, <a
|
||
href="https://lake-lab.github.io/">Human & Machine Learning Lab
|
||
(Lake Lab)</a> - <strong><em>NYU</em></strong>.</p></li>
|
||
<li><p><a href="https://as.nyu.edu/faculty/todd-gureckis.html">Todd
|
||
Gureckis</a> - <strong><em>Department of Psychology, NYU</em></strong>,
|
||
<a href="http://gureckislab.org/">Computation & Cognition Lab</a> -
|
||
<strong><em>NYU</em></strong>.</p></li>
|
||
<li><p><a href="http://www.cns.nyu.edu/malab/people.html">Wei Ji Ma</a>
|
||
- <strong><em>Department of Psychology, Center for Neural Science,
|
||
NYU</em></strong>, <a href="http://www.cns.nyu.edu/malab/">Wei Ji Ma
|
||
Lab</a> - <strong><em>NYU</em></strong>.</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="jhu">JHU</h3>
|
||
<ul>
|
||
<li><a href="https://perception.jhu.edu/chaz/">Chaz Firestone</a> -
|
||
<strong><em>Department of Psychological and Brain Sciences, Johns
|
||
Hopkins University (JHU)</em></strong>, <a
|
||
href="https://perception.jhu.edu/">Hopkins Perception & Mind Lab</a>
|
||
- <strong><em>JHU</em></strong>.</li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="sit">SIT</h3>
|
||
<ul>
|
||
<li><a href="https://markkho.github.io/">Mark Ho</a> -
|
||
<strong><em>Department of Computer Science, Stevens Institute of
|
||
Technology (SIT)</em></strong>, <a
|
||
href="https://codec-lab.github.io/">Computation and Decision-Making
|
||
Lab</a> - <strong><em>SIT</em></strong>.</li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h2 id="people-book">People & Book</h2>
|
||
<h3 id="ulf-grenander">Ulf Grenander</h3>
|
||
<p>Applied mathematician, the founder of General Pattern Theory.</p>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://www.dam.brown.edu/ptg/REPORTS/calculustext.PDF">A Calculus
|
||
of Ideas: A Mathematical Study of Thinking</a> - <strong><em>World
|
||
Scientific Publishing Company</em></strong>, 2012. [<a
|
||
href="https://scholar.google.com/scholar?cluster=12182416000849265255&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://global.oup.com/academic/product/general-pattern-theory-9780198536710?cc=lt&lang=de#">General
|
||
Pattern Theory: A Mathematical Study of Regular Structures</a> -
|
||
<strong><em>Oxford University Press</em></strong>, 1993. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=General+Pattern+Theory&btnG=">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="david-marr">David Marr</h3>
|
||
<p>Computational Cognitive Neuroscientist, the establisher of the Levels
|
||
of Analysis.</p>
|
||
<ul>
|
||
<li><a href="https://usa1lib.org/book/1223444/8e5ca8">Vision: A
|
||
Computational Investigation into the Human Representation and Processing
|
||
of Visual Information</a> - <strong><em>MIT Press</em></strong>, 1982.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=14386368570811483142&hl=en&as_sdt=0,44">All
|
||
Versions</a>].</li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="michael-tomasello">Michael Tomasello</h3>
|
||
<p>Cognitive scientist, set up the foundations of studying human
|
||
communications.</p>
|
||
<ul>
|
||
<li><p><a href="https://1lib.net/book/541274/39859f">Origins of human
|
||
communication</a> - <strong><em>MIT Press</em></strong>, 2010. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=2553369883266458474">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://hk1lib.org/book/541275/1452f8?id=541275&secret=1452f8">The
|
||
cultural origins of human cognition</a> - <strong><em>Havard University
|
||
Press</em></strong>, 2000. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=5000469061641945144">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="judea-pearl">Judea Pearl</h3>
|
||
<p>Applied mathematician, proposed causal intervention on siamese
|
||
bayesian networks.</p>
|
||
<ul>
|
||
<li><p><a href="http://bayes.cs.ucla.edu/WHY/">The Book of Why: The New
|
||
Science of Cause and Effect</a> - <strong><em>Basic Books</em></strong>,
|
||
2018. [<a
|
||
href="https://scholar.google.com/scholar?cluster=2505901292485349932&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a
|
||
href="https://hk1lib.org/book/2780725/2ec8f1?id=2780725&secret=2ec8f1">Causality:
|
||
Models, Reasoning and Inference</a> - <strong><em>Cambridge University
|
||
Press</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=10996260119229499611&hl=en&as_sdt=0,5&as_vis=1">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="susan-carey">Susan Carey</h3>
|
||
<p>Developmental psychologist, proposed <em>object</em> as a core
|
||
knowledge of human intelligence.</p>
|
||
<ul>
|
||
<li><p><a
|
||
href="https://hk1lib.org/book/844457/42178f?id=844457&secret=42178f">The
|
||
Origin of Concepts</a> - <strong><em>Oxford University
|
||
Press</em></strong>, 2009. [<a
|
||
href="https://scholar.google.com/scholar?cluster=11493102398422813821&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://hk1lib.org/book/3659332/11fa44">Conceptual
|
||
Change in Childhood</a> - <strong><em>MIT Press</em></strong>, 1985. [<a
|
||
href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=conceptual+change+in+childhood+susan+carey&btnG=">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="daniel-kahneman">Daniel Kahneman</h3>
|
||
<p>Computational cognitive scientist and Economist, set up the
|
||
foundations for Decision Theory.</p>
|
||
<ul>
|
||
<li><a
|
||
href="https://hk1lib.org/book/2181569/f5e85a?id=2181569&secret=f5e85a">Thinking,
|
||
fast and slow</a> - <strong><em>Farrar Straus Giroux</em></strong>,
|
||
2011. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=3255681708785115121">All
|
||
Versions</a>].</li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="karl-popper">Karl Popper</h3>
|
||
<p>Scientific philosophor, the founder of scientific verification
|
||
theories.</p>
|
||
<ul>
|
||
<li><p><a href="https://hk1lib.org/book/511214/299596">The logic of
|
||
scientific discovery</a> - <strong><em>Routledge</em></strong>, 2005.
|
||
[<a
|
||
href="https://scholar.google.com/scholar?cluster=5836864564733788424&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
<li><p><a href="https://hk1lib.org/book/2773070/c48f60">All Life is
|
||
Problem Solving</a> - <strong><em>Routledge</em></strong>, 2001. [<a
|
||
href="https://scholar.google.com/scholar?cluster=9799073870888093350&hl=en&as_sdt=0,5">All
|
||
Versions</a>].</p></li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h3 id="john-hopcroft">John Hopcroft</h3>
|
||
<p>Applied Mathematician, theoretical computer scientist.</p>
|
||
<ul>
|
||
<li><a
|
||
href="http://www.cs.cornell.edu/jeh/book%20no%20so;utions%20March%202019.pdf">Foundations
|
||
of Data Science</a> - <strong><em>Cambridge University
|
||
Press</em></strong>. [<a
|
||
href="https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=1802704438630899850">All
|
||
Versions</a>].</li>
|
||
</ul>
|
||
<p>*<a href="#c">Back to Top</a></p>
|
||
<h2 id="about">About</h2>
|
||
<p>The initiator of this repo has been struggling to taxonomize related
|
||
topics, since there are so many perspectives to follow, such as
|
||
task-oriented, technique-oriented, and metaphysics-oriented. Finally he
|
||
decided to focus on the perspective of <strong><em>The Sciences of
|
||
Intelligence</em></strong>—each topic describes a phenomenon of
|
||
intelligence, or an intelligent behavior—they show the objectives of
|
||
reverse-engineering human intelligence for computational methods. These
|
||
topics are never restricted to specific technical methods or tasks, but
|
||
are trying to organize the nature of intelligence—from both <em>the
|
||
software perspective</em> and <em>the hardware perspective</em>.</p>
|
||
<p>Obviously, this reading list is far from covering the every aspect of
|
||
AGI and CoCoSci. Since the list is a by-product of the literature
|
||
reviews when the initiator is working on Abduction and Bayesian
|
||
modeling, other topics are also collected with biases, more or less.
|
||
Abduction may be the way humans explain the world with the known, and
|
||
discover the unknown, requiring much more investigations into its
|
||
computational basis, cognitive underpinnings, and applications to AI.
|
||
Please feel free to reach out!</p>
|
||
<p>*<a href="#c">Back to Top</a></p>
|