update lists

terminal/deeplearningpapers

@@ -1,4 +1,4 @@
-                                                                                                 Awesome - Most Cited Deep Learning Papers
+                                                                                                      Awesome - Most Cited Deep Learning Papers
 
 !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
 
@@ -6,17 +6,16 @@
 
 A curated list of the most cited deep learning papers (2012-2016)
 
-We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep 
-learning papers which are considered as must-reads in certain research domains.
+We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning 
+papers which are considered as must-reads in certain research domains.
 
 Background
 
-Before this list, there exist other awesome deep learning lists, for example, Deep Vision (https://github.com/kjw0612/awesome-deep-vision) and Awesome Recurrent Neural Networks (https://github.com/kjw0612/awesome-rnn). Also, after this 
-list comes out, another awesome list for deep learning beginners, called Deep Learning Papers Reading Roadmap (https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap), has been created and loved by many deep learning 
-researchers.
+Before this list, there exist other awesome deep learning lists, for example, Deep Vision (https://github.com/kjw0612/awesome-deep-vision) and Awesome Recurrent Neural Networks (https://github.com/kjw0612/awesome-rnn). Also, after this list comes
+out, another awesome list for deep learning beginners, called Deep Learning Papers Reading Roadmap (https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap), has been created and loved by many deep learning researchers.
 
-Although the Roadmap List includes lots of important deep learning papers, it feels overwhelming for me to read them all. As I mentioned in the introduction, I believe that seminal works can give us lessons regardless of their 
-application domain. Thus, I would like to introduce top 100 deep learning papers here as a good starting point of overviewing deep learning researches.
+Although the Roadmap List includes lots of important deep learning papers, it feels overwhelming for me to read them all. As I mentioned in the introduction, I believe that seminal works can give us lessons regardless of their application domain.
+Thus, I would like to introduce top 100 deep learning papers here as a good starting point of overviewing deep learning researches.
 
 To get the news for newly released papers everyday, follow my twitter (https://twitter.com/TerryUm_ML) or facebook page (https://www.facebook.com/terryum.io/)! 
 
@@ -36,8 +35,8 @@
 - 2012 :  +800 citations
 - ~2012 : Old Papers (by discussion)
 
-Please note that we prefer seminal deep learning papers that can be applied to various researches rather than application papers. For that reason, some papers that meet the criteria may not be accepted while others can be. It depends on
-the impact of the paper, applicability to other researches scarcity of the research domain, and so on.
+Please note that we prefer seminal deep learning papers that can be applied to various researches rather than application papers. For that reason, some papers that meet the criteria may not be accepted while others can be. It depends on the 
+impact of the paper, applicability to other researches scarcity of the research domain, and so on.
 
 We need your contributions!
 
@@ -45,8 +44,8 @@
 (Please read the contributing guide (https://github.com/terryum/awesome-deep-learning-papers/blob/master/Contributing.md) for further instructions, though just letting me know the title of papers can also be a big contribution to us.)
 
 (Update) You can download all top-100 papers with this (https://github.com/terryum/awesome-deep-learning-papers/blob/master/fetch_papers.py) and collect all authors' names with this 
-(https://github.com/terryum/awesome-deep-learning-papers/blob/master/get_authors.py). Also, bib file (https://github.com/terryum/awesome-deep-learning-papers/blob/master/top100papers.bib) for all top-100 papers are available. Thanks, 
-doodhwala, Sven (https://github.com/sunshinemyson) and grepinsight (https://github.com/grepinsight)!
+(https://github.com/terryum/awesome-deep-learning-papers/blob/master/get_authors.py). Also, bib file (https://github.com/terryum/awesome-deep-learning-papers/blob/master/top100papers.bib) for all top-100 papers are available. Thanks, doodhwala, 
+Sven (https://github.com/sunshinemyson) and grepinsight (https://github.com/grepinsight)!
 
 + Can anyone contribute the code for obtaining the statistics of the authors of Top-100 papers?
 
@@ -79,10 +78,8 @@
 - Distilling the knowledge in a neural network (2015), G. Hinton et al. pdf  (http://arxiv.org/pdf/1503.02531)
 - Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (2015), A. Nguyen et al. pdf  (http://arxiv.org/pdf/1412.1897)
 - How transferable are features in deep neural networks? (2014), J. Yosinski et al. pdf  (http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-neural-networks.pdf)
-- CNN features off-the-Shelf: An astounding baseline for recognition (2014), A. Razavian et al. pdf  
-(http://www.cv-foundation.org//openaccess/content_cvpr_workshops_2014/W15/papers/Razavian_CNN_Features_Off-the-Shelf_2014_CVPR_paper.pdf)
-- Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. pdf  
-(http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Oquab_Learning_and_Transferring_2014_CVPR_paper.pdf)
+- CNN features off-the-Shelf: An astounding baseline for recognition (2014), A. Razavian et al. pdf  (http://www.cv-foundation.org//openaccess/content_cvpr_workshops_2014/W15/papers/Razavian_CNN_Features_Off-the-Shelf_2014_CVPR_paper.pdf)
+- Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Oquab_Learning_and_Transferring_2014_CVPR_paper.pdf)
 - Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus pdf  (http://arxiv.org/pdf/1311.2901)
 - Decaf: A deep convolutional activation feature for generic visual recognition (2014), J. Donahue et al. pdf  (http://arxiv.org/pdf/1310.1531)
 
@@ -143,8 +140,7 @@
 - Deep visual-semantic alignments for generating image descriptions (2015), A. Karpathy and L. Fei-Fei pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.pdf)
 - Show, attend and tell: Neural image caption generation with visual attention (2015), K. Xu et al. pdf  (http://arxiv.org/pdf/1502.03044)
 - Show and tell: A neural image caption generator (2015), O. Vinyals et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf)
-- Long-term recurrent convolutional networks for visual recognition and description (2015), J. Donahue et al. pdf  
-(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Donahue_Long-Term_Recurrent_Convolutional_2015_CVPR_paper.pdf)
+- Long-term recurrent convolutional networks for visual recognition and description (2015), J. Donahue et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Donahue_Long-Term_Recurrent_Convolutional_2015_CVPR_paper.pdf)
 - VQA: Visual question answering (2015), S. Antol et al. pdf  (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Antol_VQA_Visual_Question_ICCV_2015_paper.pdf)
 - DeepFace: Closing the gap to human-level performance in face verification (2014), Y. Taigman et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf):
 - Large-scale video classification with convolutional neural networks (2014), A. Karpathy et al. pdf  (http://vision.stanford.edu/pdf/karpathy14.pdf)
@@ -372,8 +368,7 @@
 
 (~2014)
 - DeepPose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.pdf)
-- Learning a Deep Convolutional Network for Image Super-Resolution (2014, C. Dong et al. pdf  
-(https://www.researchgate.net/profile/Chen_Change_Loy/publication/264552416_Lecture_Notes_in_Computer_Science/links/53e583e50cf25d674e9c280e.pdf)
+- Learning a Deep Convolutional Network for Image Super-Resolution (2014, C. Dong et al. pdf  (https://www.researchgate.net/profile/Chen_Change_Loy/publication/264552416_Lecture_Notes_in_Computer_Science/links/53e583e50cf25d674e9c280e.pdf)
 - Recurrent models of visual attention (2014), V. Mnih et al. pdf  (http://arxiv.org/pdf/1406.6247.pdf)
 - Empirical evaluation of gated recurrent neural networks on sequence modeling (2014), J. Chung et al. pdf  (https://arxiv.org/pdf/1412.3555)
 - Addressing the rare word problem in neural machine translation (2014), M. Luong et al. pdf  (https://arxiv.org/pdf/1410.8206)
@@ -399,3 +394,5 @@
 !CC0 (http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg) (https://creativecommons.org/publicdomain/zero/1.0/)
 
 To the extent possible under law, Terry T. Um (https://www.facebook.com/terryum.io/) has waived all copyright and related or neighboring rights to this work.
+
+deeplearningpapers Github: https://github.com/terryum/awesome-deep-learning-papers