Update render script and Makefile

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)
 
@@ -6,8 +6,8 @@
 
 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
 
@@ -15,8 +15,8 @@
 (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/)! 
 
@@ -37,18 +37,18 @@
 - 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!
 
 If you have any suggestions (missing papers, new papers, key researchers or typos), please feel free to edit and pull a request.
-(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.)
+(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?
 
@@ -113,7 +113,8 @@
 
 
 Convolutional Neural Network Models
-- Rethinking the inception architecture for computer vision (2016), C. Szegedy et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf)
+- Rethinking the inception architecture for computer vision (2016), C. Szegedy et al. pdf  
+(http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf)
 - Inception-v4, inception-resnet and the impact of residual connections on learning (2016), C. Szegedy et al. pdf  (http://arxiv.org/pdf/1602.07261)
 - Identity Mappings in Deep Residual Networks (2016), K. He et al. pdf  (https://arxiv.org/pdf/1603.05027v2.pdf)
 - Deep residual learning for image recognition (2016), K. He et al. pdf  (http://arxiv.org/pdf/1512.03385)
@@ -124,13 +125,16 @@
 - OverFeat: Integrated recognition, localization and detection using convolutional networks (2013), P. Sermanet et al. pdf  (http://arxiv.org/pdf/1312.6229)
 - Maxout networks (2013), I. Goodfellow et al. pdf  (http://arxiv.org/pdf/1302.4389v4)
 - Network in network (2013), M. Lin et al. pdf  (http://arxiv.org/pdf/1312.4400)
-- ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. pdf  (http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
+- ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. pdf  
+(http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
 
 
 
 Image: Segmentation / Object Detection
-- You only look once: Unified, real-time object detection (2016), J. Redmon et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf)
-- Fully convolutional networks for semantic segmentation (2015), J. Long et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)
+- You only look once: Unified, real-time object detection (2016), J. Redmon et al. pdf  
+(http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf)
+- Fully convolutional networks for semantic segmentation (2015), J. Long et al. pdf  
+(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)
 - Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. pdf  
 (http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf)
 - Fast R-CNN (2015), R. Girshick pdf  (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)
@@ -155,7 +159,8 @@
 - 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)
-- Two-stream convolutional networks for action recognition in videos (2014), K. Simonyan et al. pdf  (http://papers.nips.cc/paper/5353-two-stream-convolutional-networks-for-action-recognition-in-videos.pdf)
+- Two-stream convolutional networks for action recognition in videos (2014), K. Simonyan et al. pdf  
+(http://papers.nips.cc/paper/5353-two-stream-convolutional-networks-for-action-recognition-in-videos.pdf)
 - 3D convolutional neural networks for human action recognition (2013), S. Ji et al. pdf  (http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_JiXYY10.pdf)
 
 
@@ -181,7 +186,8 @@
 - Distributed representations of words and phrases and their compositionality (2013), T. Mikolov et al. pdf  
 (http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf)
 - Efficient estimation of word representations in vector space (2013), T. Mikolov et al.  pdf  (http://arxiv.org/pdf/1301.3781)
-- Recursive deep models for semantic compositionality over a sentiment treebank (2013), R. Socher et al. pdf  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.1327&rep=rep1&type=pdf)
+- Recursive deep models for semantic compositionality over a sentiment treebank (2013), R. Socher et al. pdf  
+(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.1327&rep=rep1&type=pdf)
 - Generating sequences with recurrent neural networks (2013), A. Graves. pdf  (https://arxiv.org/pdf/1308.0850)
 
 
@@ -190,8 +196,10 @@
 - End-to-end attention-based large vocabulary speech recognition (2016), D. Bahdanau et al. pdf  (https://arxiv.org/pdf/1508.04395)
 - Deep speech 2: End-to-end speech recognition in English and Mandarin (2015), D. Amodei et al. pdf  (https://arxiv.org/pdf/1512.02595)
 - Speech recognition with deep recurrent neural networks (2013), A. Graves pdf  (http://arxiv.org/pdf/1303.5778.pdf)
-- Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups (2012), G. Hinton et al. pdf  (http://www.cs.toronto.edu/~asamir/papers/SPM_DNN_12.pdf)
-- Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition (2012) G. Dahl et al. pdf  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.337.7548&rep=rep1&type=pdf)
+- Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups (2012), G. Hinton et al. pdf  
+(http://www.cs.toronto.edu/~asamir/papers/SPM_DNN_12.pdf)
+- Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition (2012) G. Dahl et al. pdf  
+(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.337.7548&rep=rep1&type=pdf)
 - Acoustic modeling using deep belief networks (2012), A. Mohamed et al. pdf  (http://www.cs.toronto.edu/~asamir/papers/speechDBN_jrnl.pdf)
 
 
@@ -222,7 +230,8 @@
 - Eie: Efficient inference engine on compressed deep neural network (2016), S. Han et al. pdf  (http://arxiv.org/pdf/1602.01528)
 - Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1 (2016), M. Courbariaux et al. pdf  (https://arxiv.org/pdf/1602.02830)
 - Dynamic memory networks for visual and textual question answering (2016), C. Xiong et al. pdf  (http://www.jmlr.org/proceedings/papers/v48/xiong16.pdf)
-- Stacked attention networks for image question answering (2016), Z. Yang et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_Stacked_Attention_Networks_CVPR_2016_paper.pdf)
+- Stacked attention networks for image question answering (2016), Z. Yang et al. pdf  
+(http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_Stacked_Attention_Networks_CVPR_2016_paper.pdf)
 - Hybrid computing using a neural network with dynamic external memory (2016), A. Graves et al. pdf  (https://www.gwern.net/docs/2016-graves.pdf)
 - Google's neural machine translation system: Bridging the gap between human and machine translation (2016), Y. Wu et al. pdf  (https://arxiv.org/pdf/1609.08144)
 
@@ -259,14 +268,16 @@
 (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484&rep=rep1&type=pdf)
 - Learning mid-level features for recognition (2010), Y. Boureau pdf  (http://ece.duke.edu/~lcarin/boureau-cvpr-10.pdf)
 - A practical guide to training restricted boltzmann machines (2010), G. Hinton pdf  (http://www.csri.utoronto.ca/~hinton/absps/guideTR.pdf)
-- Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio pdf  (http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_GlorotB10.pdf)
+- Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio pdf  
+(http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_GlorotB10.pdf)
 - Why does unsupervised pre-training help deep learning (2010), D. Erhan et al. pdf  (http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf)
 - Learning deep architectures for AI (2009), Y. Bengio. pdf  
 (http://sanghv.com/download/soft/machine%20learning,%20artificial%20intelligence,%20mathematics%20ebooks/ML/learning%20deep%20architectures%20for%20AI%20(2009).pdf)
 - Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (2009), H. Lee et al. pdf  
 (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.149.802&rep=rep1&type=pdf)
 - Greedy layer-wise training of deep networks (2007), Y. Bengio et al. pdf  (http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_739.pdf)
-- Reducing the dimensionality of data with neural networks, G. Hinton and R. Salakhutdinov. pdf  (http://homes.mpimf-heidelberg.mpg.de/~mhelmsta/pdf/2006%20Hinton%20Salakhudtkinov%20Science.pdf)
+- Reducing the dimensionality of data with neural networks, G. Hinton and R. Salakhutdinov. pdf  
+(http://homes.mpimf-heidelberg.mpg.de/~mhelmsta/pdf/2006%20Hinton%20Salakhudtkinov%20Science.pdf)
 - A fast learning algorithm for deep belief nets (2006), G. Hinton et al. pdf  (http://nuyoo.utm.mx/~jjf/rna/A8%20A%20fast%20learning%20algorithm%20for%20deep%20belief%20nets.pdf)
 - Gradient-based learning applied to document recognition (1998), Y. LeCun et al. pdf  (http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf)
 - Long short-term memory (1997), S. Hochreiter and J. Schmidhuber. pdf  (http://www.mitpressjournals.org/doi/pdfplus/10.1162/neco.1997.9.8.1735)
@@ -346,7 +357,8 @@
 (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Bell_Inside-Outside_Net_Detecting_CVPR_2016_paper.pdf).
 - Instance-aware semantic segmentation via multi-task network cascades (2016), J. Dai et al. pdf  
 (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Dai_Instance-Aware_Semantic_Segmentation_CVPR_2016_paper.pdf)
-- Conditional image generation with pixelcnn decoders (2016), A. van den Oord et al. pdf  (http://papers.nips.cc/paper/6527-tree-structured-reinforcement-learning-for-sequential-object-localization.pdf)
+- Conditional image generation with pixelcnn decoders (2016), A. van den Oord et al. pdf  
+(http://papers.nips.cc/paper/6527-tree-structured-reinforcement-learning-for-sequential-object-localization.pdf)
 - Deep networks with stochastic depth (2016), G. Huang et al., pdf  (https://arxiv.org/pdf/1603.09382)
 - Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics (2016), Yee Whye Teh et al. pdf  (http://www.jmlr.org/papers/volume17/teh16a/teh16a.pdf)
 
@@ -391,7 +403,8 @@
 
 
 (~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)
+- 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)
 - Recurrent models of visual attention (2014), V. Mnih et al. pdf  (http://arxiv.org/pdf/1406.6247.pdf)
@@ -401,7 +414,8 @@
 - Recurrent neural network regularization (2014), W. Zaremba et al. pdf  (http://arxiv.org/pdf/1409.2329)
 - Intriguing properties of neural networks (2014), C. Szegedy et al. pdf  (https://arxiv.org/pdf/1312.6199.pdf)
 - Towards end-to-end speech recognition with recurrent neural networks (2014), A. Graves and N. Jaitly. pdf  (http://www.jmlr.org/proceedings/papers/v32/graves14.pdf)
-- Scalable object detection using deep neural networks (2014), D. Erhan et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Erhan_Scalable_Object_Detection_2014_CVPR_paper.pdf)
+- Scalable object detection using deep neural networks (2014), D. Erhan et al. pdf  
+(http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Erhan_Scalable_Object_Detection_2014_CVPR_paper.pdf)
 - On the importance of initialization and momentum in deep learning (2013), I. Sutskever et al. pdf  (http://machinelearning.wustl.edu/mlpapers/paper_files/icml2013_sutskever13.pdf)
 - Regularization of neural networks using dropconnect (2013), L. Wan et al. pdf  (http://machinelearning.wustl.edu/mlpapers/paper_files/icml2013_wan13.pdf)
 - Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al. pdf  (https://hal-enpc.archives-ouvertes.fr/docs/00/74/20/77/PDF/farabet-pami-13.pdf)
@@ -413,7 +427,8 @@
 
 Acknowledgement
 
-Thank you for all your contributions. Please make sure to read the contributing guide (https://github.com/terryum/awesome-deep-learning-papers/blob/master/Contributing.md) before you make a pull request.
+Thank you for all your contributions. Please make sure to read the contributing guide (https://github.com/terryum/awesome-deep-learning-papers/blob/master/Contributing.md) before you make a 
+pull request.
 
 License
 !CC0 (http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg) (https://creativecommons.org/publicdomain/zero/1.0/)