564 lines
24 KiB
HTML
564 lines
24 KiB
HTML
<h1 id="awesome-random-forest">Awesome Random Forest</h1>
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<p>Random Forest - a curated list of resources regarding tree-based
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methods and more, including but not limited to random forest, bagging
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and boosting.</p>
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<h2 id="contributing">Contributing</h2>
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<p>Please feel free to <a
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href="https://github.com/kjw0612/awesome-random-forest/pulls">pull
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requests</a>.</p>
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<p>The project is not actively maintained.</p>
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<p><a
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href="https://gitter.im/kjw0612/awesome-random-forest?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge"><img
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src="https://badges.gitter.im/Join%20Chat.svg"
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alt="Join the chat at https://gitter.im/kjw0612/awesome-random-forest" /></a></p>
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<figure>
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<img
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src="https://31.media.tumblr.com/79670eabe93cdd448c15f5bcb198d0fb/tumblr_inline_n8e398YbKv1s04rc3.png"
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alt="randomforest" />
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<figcaption aria-hidden="true">randomforest</figcaption>
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</figure>
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<h2 id="table-of-contents">Table of Contents</h2>
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<ul>
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<li><a href="#codes">Codes</a> (#codes)</li>
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<li><a href="#theory">Theory</a>
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<ul>
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<li><a href="#lectures">Lectures</a></li>
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<li><a href="#books">Books</a></li>
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<li><a href="#papers">Papers</a> (#papers)
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<ul>
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<li><a href="#analysis-understanding">Analysis / Understanding</a>
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(#analysis–understanding)</li>
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<li><a href="#model-variants">Model variants</a> (#model-variants)</li>
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</ul></li>
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<li><a href="#thesis">Thesis</a> (#thesis)</li>
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</ul></li>
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<li><a href="#applications">Applications</a> (#applications)
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<ul>
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<li><a href="#image-classification">Image Classification</a>
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(#image-classification)</li>
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<li><a href="#object-detection">Object Detection</a>
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(#object-detection)</li>
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<li><a href="#object-tracking">Object Tracking</a>
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(#object-tracking)</li>
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<li><a href="#edge-detection">Edge Detection</a> (#edge-detection)</li>
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<li><a href="#semantic-segmentation">Semantic Segmentation</a>
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(#semantic-segmentation)</li>
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<li><a href="#human-hand-pose-estimation">Human / Hand Pose
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Estimation</a> (#human–hand-pose-estimation)</li>
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<li><a href="#d-localization">3D Localization</a>
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(#3d-localization)</li>
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<li><a href="#low-level-vision">Low-Level Vision</a>
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(#low-level-vision)</li>
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<li><a href="#facial-expression-recognition">Facial Expression
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Recognition</a> (#facial-expression-recognition)</li>
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<li><a
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href="#Interpretability,%20regularization,%20compression%20pruning%20and%20feature%20selection">Interpretability,
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regularization, compression pruning and feature selection</a></li>
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</ul></li>
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</ul>
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<h2 id="codes">Codes</h2>
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<ul>
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<li>Matlab
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<ul>
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<li>[Piotr Dollar’s toolbox]
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(http://vision.ucsd.edu/~pdollar/toolbox/doc/)</li>
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<li>[Andrej Karpathy’s toolbox]
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(https://github.com/karpathy/Random-Forest-Matlab)</li>
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<li>[M5PrimeLab by Gints Jekabsons]
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(http://www.cs.rtu.lv/jekabsons/regression.html)</li>
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</ul></li>
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<li>R
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<ul>
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<li>[Breiman and Cutler’s random forests]
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(http://cran.r-project.org/web/packages/randomForest/)</li>
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<li><a href="http://cran.r-project.org/web/packages/party/">Hothorn et
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al.’s party package with <code>cforest</code> function</a></li>
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</ul></li>
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<li>C/C++
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<ul>
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<li>[Sherwood library]
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(http://research.microsoft.com/en-us/downloads/52d5b9c3-a638-42a1-94a5-d549e2251728/)</li>
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<li>[Regression tree package by Pierre Geurts]
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(http://www.montefiore.ulg.ac.be/~geurts/Software.html)</li>
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<li>[ranger: A Fast Implementation of Random Forests]
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(https://github.com/imbs-hl/ranger)</li>
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</ul></li>
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<li>Python
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<ul>
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<li>[Scikit-learn]
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(http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble)</li>
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</ul></li>
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<li>JavaScript
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<ul>
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<li>[Forestjs] (https://github.com/karpathy/forestjs)</li>
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</ul></li>
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<li>Go (golang)
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<ul>
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<li>[CloudForest] (https://github.com/ryanbressler/CloudForest)</li>
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</ul></li>
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</ul>
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<h2 id="theory">Theory</h2>
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<h3 id="lectures">Lectures</h3>
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<ul>
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<li>[ICCV 2013 Tutorial : Decision Forests and Fields for Computer
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Vision]
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(http://research.microsoft.com/en-us/um/cambridge/projects/iccv2013tutorial/)
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by Jamie Shotton and Sebastian Nowozin
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<ul>
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<li>[Lecture 1]
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(http://techtalks.tv/talks/randomized-decision-forests-and-their-applications-in-computer-vision-jamie/59432/)
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: Randomized Decision Forests and their Applications in Computer Vision
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I (Decision Forest, Classification Forest,</li>
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<li>[Lecture 2]
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(http://techtalks.tv/talks/decision-jungles-jamie-second-half-of-above/59434/)
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: Randomized Decision Forests and their Applications in Computer Vision
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II (Regression Forest, Decision Jungle)</li>
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<li>[Lecture 3]
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(http://techtalks.tv/talks/entropy-estimation-and-streaming-data-sebastian/59433/)
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: Entropy estimation and streaming data</li>
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<li>[Lecture 4]
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(http://techtalks.tv/talks/decision-and-regression-tree-fields-sebastian/59435/)
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: Decision and Regression Tree Fields</li>
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</ul></li>
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<li>[UBC Machine Learning]
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(http://www.cs.ubc.ca/~nando/540-2013/lectures.html) by Nando de Freitas
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<ul>
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<li>[Lecture 8 slide]
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(http://www.cs.ubc.ca/~nando/540-2013/lectures/l8.pdf) , [Lecture 8
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video]
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(https://www.youtube.com/watch?v=-dCtJjlEEgM&list=PLE6Wd9FR–EdyJ5lbFl8UuGjecvVw66F6&index=11)
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: Decision trees</li>
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<li>[Lecture 9 slide]
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(http://www.cs.ubc.ca/~nando/540-2013/lectures/l9.pdf) , [Lecture 9
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video]
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(https://www.youtube.com/watch?v=3kYujfDgmNk&list=PLE6Wd9FR–EdyJ5lbFl8UuGjecvVw66F6&index=12)
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: Random forests</li>
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<li>[Lecture 10 video]
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(https://www.youtube.com/watch?v=zFGPjRPwyFw&index=13&list=PLE6Wd9FR–EdyJ5lbFl8UuGjecvVw66F6)
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: Random forest applications</li>
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</ul></li>
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</ul>
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<h3 id="books">Books</h3>
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<ul>
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<li>Antonio Criminisi, Jamie Shotton (2013)
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<ul>
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<li>[Decision Forests for Computer Vision and Medical Image Analysis]
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(http://link.springer.com/book/10.1007%2F978-1-4471-4929-3)</li>
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</ul></li>
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<li>Trevor Hastie, Robert Tibshirani, Jerome Friedman (2008)
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<ul>
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<li>[The Elements of Statistical Learning, (Chapter 10, 15, and 16)]
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(http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf)</li>
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</ul></li>
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<li>Luc Devroye, Laszlo Gyorfi, Gabor Lugosi (1996)
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<ul>
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<li><a href="http://www.szit.bme.hu/~gyorfi/pbook.pdf">A Probabilistic
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Theory of Pattern Recognition (Chapter 20, 21)</a></li>
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</ul></li>
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</ul>
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<h3 id="papers">Papers</h3>
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<h4 id="analysis-understanding">Analysis / Understanding</h4>
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<ul>
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<li>Consistency of random forests <a
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href="http://www.normalesup.org/~scornet/paper/article.pdf">[Paper]</a></li>
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<li>Scornet, E., Biau, G. and Vert, J.-P. (2015). Consistency of random
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forests, The Annals of Statistics, in press.</li>
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<li>On the asymptotics of random forests <a
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href="http://arxiv.org/abs/1409.2090">[Paper]</a></li>
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<li>Scornet, E. (2015). On the asymptotics of random forests, Journal of
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Multivariate Analysis, in press.</li>
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<li>Random Forests In Theory and In Practice [[Paper]
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(http://jmlr.org/proceedings/papers/v32/denil14.pdf)]
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<ul>
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<li>Misha Denil, David Matheson, Nando de Freitas, Narrowing the Gap:
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Random Forests In Theory and In Practice, ICML 2014</li>
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</ul></li>
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<li>Explaining the Success of AdaBoost and Random Forests as
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Interpolating Classifiers Abraham J. Wyner, Matthew Olson, Justin
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Bleich, David Mease [<a
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href="https://arxiv.org/abs/1504.07676">Paper</a>]</li>
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</ul>
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<h4 id="model-variants">Model variants</h4>
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<ul>
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<li>Deep Neural Decision Forests [<a
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href="https://www.microsoft.com/en-us/research/publication/deep-neural-decision-forests/">Paper</a>]
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<ul>
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<li>Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samuel
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Rota Bulo, Deep Neural Decision Forests, ICCV 2015</li>
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</ul></li>
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<li>Canonical Correlation Forests [<a
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href="http://arxiv.org/pdf/1507.05444.pdf">Paper</a>]
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<ul>
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<li>Tom Rainforth, and Frank Wood, Canonical Correlation Forests, arxiv
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2015</li>
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</ul></li>
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<li>Relating Cascaded Random Forests to Deep Convolutional Neural
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Networks [[Paper] (http://arxiv.org/pdf/1507.07583.pdf)]
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<ul>
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<li>David L Richmond, Dagmar Kainmueller, Michael Y Yang, Eugene W
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Myers, and Carsten Rother, Relating Cascaded Random Forests to Deep
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Convolutional Neural Networks for Semantic Segmentation, arxiv 2015</li>
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</ul></li>
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<li>Bayesian Forests [[Paper]
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(http://jmlr.org/proceedings/papers/v37/matthew15.pdf)]
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<ul>
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<li>Taddy Matthew, Chun-Sheng Chen, Jun Yu, Mitch Wyle, Bayesian and
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Empirical Bayesian Forests, ICML 2015</li>
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</ul></li>
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<li>Mondrian Forests: Efficient Online Random Forests <a
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href="http://www.gatsby.ucl.ac.uk/~balaji/mondrian_forests_nips14.pdf">[Paper]</a>
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<a href="http://www.gatsby.ucl.ac.uk/~balaji/mondrianforest/">[Code]</a>
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<a
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href="http://www.gatsby.ucl.ac.uk/~balaji/mondrian_forests_slides.pdf">[Slides]</a>
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<ul>
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<li>Balaji Lakshminarayanan, Daniel M. Roy and Yee Whye Teh, Mondrian
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Forests: Efficient Online Random Forests, NIPS 2014</li>
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</ul></li>
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<li>Extremely randomized trees P Geurts, D Ernst, L Wehenkel - Machine
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learning, 2006 [<a
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href="http://orbi.ulg.be/bitstream/2268/9357/1/geurts-mlj-advance.pdf">Paper</a>]
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[[Code]
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(http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html)]</li>
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<li>Decision Jungles [[Paper]
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(http://research.microsoft.com/pubs/205439/DecisionJunglesNIPS2013.pdf)]
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<ul>
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<li>Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John
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Winn, and Antonio Criminisi, Decision Jungles: Compact and Rich Models
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for Classification, NIPS 2013</li>
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<li>Laptev, Dmitry, and Joachim M. Buhmann. Transformation-invariant
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convolutional jungles. CVPR 2015. [<a
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href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Laptev_Transformation-Invariant_Convolutional_Jungles_2015_CVPR_paper.pdf">Paper</a>]</li>
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</ul></li>
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<li>Semi-supervised Node Splitting for Random Forest Construction
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[[Paper]
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(http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Liu_Semi-supervised_Node_Splitting_2013_CVPR_paper.pdf)]
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<ul>
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<li>Xiao Liu, Mingli Song, Dacheng Tao, Zicheng Liu, Luming Zhang, Chun
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Chen and Jiajun Bu, Semi-supervised Node Splitting for Random Forest
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Construction, CVPR 2013</li>
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</ul></li>
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<li>Improved Information Gain Estimates for Decision Tree Induction
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[[Paper]
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(http://www.nowozin.net/sebastian/papers/nowozin2012infogain.pdf)]
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<ul>
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<li>Sebastian Nowozin, Improved Information Gain Estimates for Decision
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Tree Induction, ICML 2012</li>
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</ul></li>
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<li>MIForests: Multiple-Instance Learning with Randomized Trees [[Paper]
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(http://lrs.icg.tugraz.at/pubs/leistner_eccv_10.pdf)] [[Code]
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(http://www.ymer.org/amir/software/milforests/)]
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<ul>
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<li>Christian Leistner, Amir Saffari, and Horst Bischof, MIForests:
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Multiple-Instance Learning with Randomized Trees, ECCV 2010</li>
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</ul></li>
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<li>Samuel Schulter, Paul Wohlhart, Christian Leistner, Amir Saffari,
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Peter M. Roth, Horst Bischof: Alternating Decision Forests. CVPR 2013 <a
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href="http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Schulter_Alternating_Decision_Forests_2013_CVPR_paper.pdf">Paper</a></li>
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<li>Decision Forests, Convolutional Networks and the Models in-Between
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[<a href="https://arxiv.org/abs/1603.01250">Paper</a>]</li>
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<li>Random Uniform Forests Saïp Ciss [<a
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href="https://hal.archives-ouvertes.fr/hal-01104340/">Paper</a>] [<a
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href="https://cran.r-project.org/web/packages/randomUniformForest/index.html">Code
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R</a>]</li>
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<li>Autoencoder Trees, Ozan İrsoy, Ethem Alpaydın 2015 [<a
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href="http://www.jmlr.org/proceedings/papers/v45/Irsoy15.pdf">Paper</a>]</li>
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</ul>
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<h2 id="thesis">Thesis</h2>
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<ul>
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<li>Understanding Random Forests</li>
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<li>PhD dissertation, Gilles Louppe, July 2014. Defended on October 9,
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2014.</li>
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<li><a href="https://github.com/glouppe/phd-thesis">[Repository]</a>
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with thesis and related codes</li>
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</ul>
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<h2 id="applications">Applications</h2>
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<h3 id="image-classification">Image classification</h3>
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<ul>
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<li>ETH Zurich [[Paper-CVPR15]
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(http://www.iai.uni-bonn.de/~gall/download/jgall_coarse2fine_cvpr15.pdf)]
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[[Paper-CVPR14]
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(http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Ristin_Incremental_Learning_of_2014_CVPR_paper.pdf)]
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[[Paper-ECCV]
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(http://www.vision.ee.ethz.ch/~lbossard/bossard_eccv14_food-101.pdf)]
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<ul>
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<li>Marko Ristin, Juergen Gall, Matthieu Guillaumin, and Luc Van Gool,
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From Categories to Subcategories: Large-scale Image Classification with
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Partial Class Label Refinement, CVPR 2015</li>
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<li>Marko Ristin, Matthieu Guillaumin, Juergen Gall, and Luc Van Gool,
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Incremental Learning of NCM Forests for Large-Scale Image
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Classification, CVPR 2014</li>
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<li>Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool, Food-101 –
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Mining Discriminative Components with Random Forests, ECCV 2014</li>
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</ul></li>
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<li>University of Girona & University of Oxford [[Paper]
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(http://www.cs.huji.ac.il/~daphna/course/CoursePapers/bosch07a.pdf)]
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<ul>
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<li>Anna Bosch, Andrew Zisserman, and Xavier Munoz, Image Classification
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using Random Forests and Ferns, ICCV 2007</li>
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</ul></li>
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</ul>
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<h3 id="object-detection">Object Detection</h3>
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<ul>
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<li>Graz University of Technology [[Paper-CVPR]
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(http://lrs.icg.tugraz.at/pubs/schulter_cvpr_14.pdf)] [[Paper-ICCV]
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(http://lrs.icg.tugraz.at/pubs/schulter_iccv_13.pdf)]
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<ul>
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<li>Samuel Schulter, Christian Leistner, Paul Wohlhart, Peter M. Roth,
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and Horst Bischof, Accurate Object Detection with Joint
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Classification-Regression Random Forests, CVPR 2014</li>
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<li>Samuel Schulter, Christian Leistner, Paul Wohlhart, Peter M. Roth,
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and Horst Bischof, Alternating Regression Forests for Object Detection
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and Pose Estimation, ICCV 2013</li>
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</ul></li>
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<li>ETH Zurich + Microsoft Research Cambridge [[Paper]
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(http://www.iai.uni-bonn.de/~gall/download/jgall_houghforest_cvpr09.pdf)]
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<ul>
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<li>Juergen Gall, and Victor Lempitsky, Class-Specific Hough Forests for
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Object Detection, CVPR 2009</li>
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</ul></li>
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</ul>
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<h3 id="object-tracking">Object Tracking</h3>
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<ul>
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<li>Technische Universitat Munchen [[Paper]
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(http://campar.in.tum.de/pub/tanda2014cvpr/tanda2014cvpr.pdf)]
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<ul>
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<li>David Joseph Tan, and Slobodan Ilic, Multi-Forest Tracker: A
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Chameleon in Tracking, CVPR 2014</li>
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</ul></li>
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<li>ETH Zurich + Leibniz University Hannover + Stanford University
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[[Paper]
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(http://www.igp.ethz.ch/photogrammetry/publications/pdf_folder/LeaFenKuzRosSavCVPR14.pdf)]
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<ul>
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<li>Laura Leal-Taixe, Michele Fenzi, Alina Kuznetsova, Bodo Rosenhahn,
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and Silvio Savarese, Learning an image-based motion context for multiple
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people tracking, CVPR 2014</li>
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</ul></li>
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<li>Graz University of Technology [[Paper]
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(https://lrs.icg.tugraz.at/pubs/godec_iccv_11.pdf)]
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<ul>
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<li>Martin Godec, Peter M. Roth, and Horst Bischof, Hough-based Tracking
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of Non-Rigid Objects, ICCV 2011</li>
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</ul></li>
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</ul>
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<h3 id="edge-detection">Edge Detection</h3>
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<ul>
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<li>University of California, Irvine [[Paper]
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(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hallman_Oriented_Edge_Forests_2015_CVPR_paper.pdf)]
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[[Code] (https://github.com/samhallman/oef)]
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<ul>
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<li>Sam Hallman, and Charless C. Fowlkes, Oriented Edge Forests for
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Boundary Detection, CVPR 2015</li>
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</ul></li>
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<li>Microsoft Research [[Paper]
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(http://research-srv.microsoft.com/pubs/202540/DollarICCV13edges.pdf)]
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[[Code] (https://github.com/pdollar/edges)]
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<ul>
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<li>Piotr Dollar, and C. Lawrence Zitnick, Structured Forests for Fast
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Edge Detection, ICCV 2013</li>
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</ul></li>
|
||
<li>Massachusetts Inst. of Technology + Microsoft Research [[Paper]
|
||
(http://research.microsoft.com/en-us/um/people/larryz/cvpr13sketchtokens.pdf)]
|
||
[[Code] (https://github.com/joelimlimit/SketchTokens)]
|
||
<ul>
|
||
<li>Joseph J. Lim, C. Lawrence Zitnick, and Piotr Dollar, Sketch Tokens:
|
||
A Learned Mid-level Representation for Contour and Object Detection,
|
||
CVPR 2013</li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3 id="semantic-segmentation">Semantic Segmentation</h3>
|
||
<ul>
|
||
<li>Fondazione Bruno Kessler, Microsoft Research Cambridge [[Paper]
|
||
(http://www.dsi.unive.it/~srotabul/files/publications/CVPR2014a.pdf)]
|
||
<ul>
|
||
<li>Samuel Rota Bulo, and Peter Kontschieder, Neural Decision Forests
|
||
for Semantic Image Labelling, CVPR 2014</li>
|
||
</ul></li>
|
||
<li>INRIA + Microsoft Research Cambridge [[Paper]
|
||
(http://step.polymtl.ca/~rv101/MICCAI-Laplacian-Forest.pdf)]
|
||
<ul>
|
||
<li>Herve Lombaert, Darko Zikic, Antonio Criminisi, and Nicholas Ayache,
|
||
Laplacian Forests:Semantic Image Segmentation by Guided Bagging, MICCAI
|
||
2014</li>
|
||
</ul></li>
|
||
<li>Microsoft Research Cambridge + GE Global Research Center +
|
||
University of California + Rutgers Univeristy [[Paper]
|
||
(http://research.microsoft.com/pubs/146430/criminisi_ipmi_2011c.pdf)]
|
||
<ul>
|
||
<li>Albert Montillo1, Jamie Shotton, John Winn, Juan Eugenio Iglesias,
|
||
Dimitri Metaxas, and Antonio Criminisi, Entangled Decision Forests and
|
||
their Application for Semantic Segmentation of CT Images, IPMI 2011</li>
|
||
</ul></li>
|
||
<li>University of Cambridge + Toshiba Corporate R&D Center [[Paper]
|
||
(http://mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2008-CVPR-semantic-texton-forests.pdf)]
|
||
<ul>
|
||
<li>Jamie Shotton, Matthew Johnson, and Roberto Cipolla, Semantic Texton
|
||
Forests for Image Categorization and Segmentation, CVPR 2008</li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3 id="human-hand-pose-estimation">Human / Hand Pose Estimation</h3>
|
||
<ul>
|
||
<li>Microsoft Research Cambridge [[Paper-CHI]
|
||
(http://research.microsoft.com/pubs/238453/pn362-sharp.pdf)][[Video-CHI]
|
||
(http://research.microsoft.com/pubs/238453/pn362-sharp-video.mp4)]
|
||
[[Paper-CVPR] (http://research.microsoft.com/pubs/162510/vm.pdf)]
|
||
<ul>
|
||
<li>Toby Sharp, Cem Keskin, Duncan Robertson, Jonathan Taylor, Jamie
|
||
Shotton, David Kim, Christoph Rhemann, Ido Leichter, Alon Vinnikov,
|
||
Yichen Wei, Daniel Freedman, Pushmeet Kohli, Eyal Krupka, Andrew
|
||
Fitzgibbon, and Shahram Izadi, Accurate, Robust, and Flexible Real-time
|
||
Hand Tracking, CHI 2015</li>
|
||
<li>Jonathan Taylor, Jamie Shotton, Toby Sharp, and Andrew Fitzgibbon,
|
||
The Vitruvian Manifold:Inferring Dense Correspondences for One-Shot
|
||
Human Pose Estimation, CVPR 2012</li>
|
||
</ul></li>
|
||
<li>Microsoft Research Haifa [[Paper]
|
||
(http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Krupka_Discriminative_Ferns_Ensemble_2014_CVPR_paper.pdf)]
|
||
<ul>
|
||
<li>Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, and Daniel
|
||
Freedman, Discriminative Ferns Ensemble for Hand Pose Recognition, CVPR
|
||
2014</li>
|
||
</ul></li>
|
||
<li>Microsoft Research Asia [[Paper]
|
||
(http://research.microsoft.com/en-us/people/yichenw/cvpr14_facealignment.pdf)]
|
||
<ul>
|
||
<li>Shaoqing Ren, Xudong Cao, Yichen Wei, and Jian Sun, Face Alignment
|
||
at 3000 FPS via Regressing Local Binary Features, CVPR 2014</li>
|
||
</ul></li>
|
||
<li>Imperial College London [[Paper-CVPR-Face]
|
||
(http://www.iis.ee.ic.ac.uk/icvl/doc/cvpr14_xiaowei.pdf)]
|
||
[[Paper-CVPR-Hand]
|
||
(http://www.iis.ee.ic.ac.uk/icvl/doc/cvpr14_danny.pdf)] [[Paper-ICCV]
|
||
(http://www.iis.ee.ic.ac.uk/icvl/doc/ICCV13_danny.pdf)]
|
||
<ul>
|
||
<li>Xiaowei Zhao, Tae-Kyun Kim, and Wenhan Luo, Unified Face Analysis by
|
||
Iterative Multi-Output Random Forests, CVPR 2014</li>
|
||
<li>Danhang Tang, Hyung Jin Chang, Alykhan Tejani, and Tae-Kyun Kim,
|
||
Latent Regression Forest: Structured Estimation of 3D Articulated Hand
|
||
Posture, CVPR 2014</li>
|
||
<li>Danhang Tang, Tsz-Ho Yu, and Tae-Kyun Kim, Real-time Articulated
|
||
Hand Pose Estimation using Semi-supervised Transductive Regression
|
||
Forests, ICCV 2013</li>
|
||
</ul></li>
|
||
<li>ETH Zurich + Microsoft [[Paper]
|
||
(https://lirias.kuleuven.be/bitstream/123456789/398648/2/3601_open+access.pdf)]
|
||
<ul>
|
||
<li>Matthias Dantone, Juergen Gall, Christian Leistner, and Luc Van
|
||
Gool, Human Pose Estimation using Body Parts Dependent Joint Regressors,
|
||
CVPR 2013</li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3 id="d-localization">3D localization</h3>
|
||
<ul>
|
||
<li>Imperial College London [[Paper]
|
||
(http://www.iis.ee.ic.ac.uk/icvl/doc/ECCV2014_aly.pdf)]
|
||
<ul>
|
||
<li>Alykhan Tejani, Danhang Tang, Rigas Kouskouridas, and Tae-Kyun Kim,
|
||
Latent-Class Hough Forests for 3D Object Detection and Pose Estimation,
|
||
ECCV 2014</li>
|
||
</ul></li>
|
||
<li>Microsoft Research Cambridge + University of Illinois + Imperial
|
||
College London [[Paper]
|
||
(http://abnerguzman.com/publications/gkgssfi_cvpr14.pdf)]
|
||
<ul>
|
||
<li>Abner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton,
|
||
Toby Sharp, Andrew Fitzgibbon, and Shahram Izadi, Multi-Output Learning
|
||
for Camera Relocalization, CVPR 2014</li>
|
||
</ul></li>
|
||
<li>Microsoft Research Cambridge [[Paper]
|
||
(http://research.microsoft.com/pubs/184826/relocforests.pdf)]
|
||
<ul>
|
||
<li>Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio
|
||
Criminisi, and Andrew Fitzgibbon, Scene Coordinate Regression Forests
|
||
for Camera Relocalization in RGB-D Images, CVPR 2013</li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3 id="low-level-vision">Low-Level vision</h3>
|
||
<ul>
|
||
<li>Super-Resolution
|
||
<ul>
|
||
<li>Technicolor R&I Hannover [<a
|
||
href="https://technicolor-my.sharepoint.com/personal/jordi_salvador_technicolor_com/_layouts/15/guestaccess.aspx?guestaccesstoken=2z88Le9arMQ7tcGGYApHmdM9Pet2AqqoxMBDcu6eRbc%3d&docid=0e7f0b9ed1d0f4497829ae6b2b0deeec3">Paper</a>]
|
||
<ul>
|
||
<li>Jordi Salvador, and Eduardo Pérez-Pellitero, Naive Bayes
|
||
Super-Resolution Forest, ICCV 2015</li>
|
||
</ul></li>
|
||
<li>Graz University of Technology [[Paper]
|
||
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schulter_Fast_and_Accurate_2015_CVPR_paper.pdf)]
|
||
<ul>
|
||
<li>Samuel Schulter, Christian Leistner, and Horst Bischof, Fast and
|
||
Accurate Image Upscaling with Super-Resolution Forests, CVPR 2015</li>
|
||
</ul></li>
|
||
</ul></li>
|
||
<li>Denoising
|
||
<ul>
|
||
<li>Microsoft Research + iCub Facility - Istituto Italiano di Tecnologia
|
||
[[Paper]
|
||
(http://research.microsoft.com/pubs/217099/CVPR2014ForestFiltering.pdf)]
|
||
<ul>
|
||
<li>Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie
|
||
Shotton, Antonio Criminisi, Ugo Pattacini, and Tim Paek, Filter Forests
|
||
for Learning Data-Dependent Convolutional Kernels, CVPR 2014</li>
|
||
</ul></li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3 id="facial-expression-recognition">Facial expression
|
||
recognition</h3>
|
||
<ul>
|
||
<li>Sorbonne Universites [<a
|
||
href="http://www.isir.upmc.fr/files/2015ACTI3549.pdf">Paper</a>]
|
||
<ul>
|
||
<li>Arnaud Dapogny, Kevin Bailly, and Severine Dubuisson, Pairwise
|
||
Conditional Random Forests for Facial Expression Recognition, ICCV
|
||
2015</li>
|
||
</ul></li>
|
||
</ul>
|
||
<h3
|
||
id="interpretability-regularization-compression-pruning-and-feature-selection">Interpretability,
|
||
regularization, compression pruning and feature selection</h3>
|
||
<ul>
|
||
<li>Global Refinement of Random Forest [[Paper]
|
||
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ren_Global_Refinement_of_2015_CVPR_paper.pdf)]
|
||
<ul>
|
||
<li>Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun, Global Refinement of
|
||
Random Forest, CVPR 2015</li>
|
||
</ul></li>
|
||
<li>L1-based compression of random forest models Arnaud Joly, Fran¸cois
|
||
Schnitzler, Pierre Geurts and Louis Wehenkel ESANN 2012 [<a
|
||
href="https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2012-43.pdf">Paper</a>]</li>
|
||
<li>Feature-Budgeted Random Forest [[Paper]
|
||
(http://jmlr.org/proceedings/papers/v37/nan15.pdf)] [<a
|
||
href="http://jmlr.org/proceedings/papers/v37/nan15-supp.pdf">Supp</a>]
|
||
<ul>
|
||
<li>Feng Nan, Joseph Wang, Venkatesh Saligrama, Feature-Budgeted Random
|
||
Forest, ICML 2015</li>
|
||
<li>Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph
|
||
Wang, Venkatesh Saligrama NIPS 2016 [<a
|
||
href="https://papers.nips.cc/paper/6250-pruning-random-forests-for-prediction-on-a-budget.pdf">Paper</a>]</li>
|
||
</ul></li>
|
||
<li>Meinshausen, Nicolai. “Node harvest.” The Annals of Applied
|
||
Statistics 4.4 (2010): 2049-2072. [<a
|
||
href="http://projecteuclid.org/download/pdfview_1/euclid.aoas/1294167809">Paper</a>]
|
||
[<a
|
||
href="https://cran.r-project.org/web/packages/nodeHarvest/index.html">Code
|
||
R</a>] [<a href="https://github.com/mbillingr/NodeHarvest">Code
|
||
Python</a>]</li>
|
||
<li>Making Tree Ensembles Interpretable: A Bayesian Model Selection
|
||
Approach S. Hara, K. Hayashi, [<a
|
||
href="https://arxiv.org/abs/1606.09066">Paper</a>] [<a
|
||
href="https://github.com/sato9hara/defragTrees">Code</a>]</li>
|
||
<li>Cui, Zhicheng, et al. “Optimal action extraction for random forests
|
||
and boosted trees.” ACM SIGKDD 2015. [<a
|
||
href="http://www.cse.wustl.edu/~ychen/public/OAE.pdf">Paper</a>]</li>
|
||
<li>DART: Dropouts meet Multiple Additive Regression Trees K. V. Rashmi,
|
||
Ran Gilad-Bachrach [<a
|
||
href="http://www.jmlr.org/proceedings/papers/v38/korlakaivinayak15.pdf">Paper</a>]</li>
|
||
<li>Begon, Jean-Michel, Arnaud Joly, and Pierre Geurts. Joint learning
|
||
and pruning of decision forests. (2016). [<a
|
||
href="http://orbi.ulg.ac.be/bitstream/2268/202344/1/Begon_jlpdf_abstract.pdf">Paper</a>]</li>
|
||
</ul>
|
||
<p>Maintainers - <a href="http://github.com/kjw0612">Jiwon Kim</a>, <a
|
||
href="http://github.com/deruci">Jung Kwon Lee</a></p>
|
||
<p><a
|
||
href="https://github.com/kjw0612/awesome-random-forest">randomforest.md
|
||
Github</a></p>
|