Updating conversion, creating readmes

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)
 
@@ -6,25 +6,24 @@
 
 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/)! 
 
 Awesome list criteria
 
 1. A list of top 100 deep learning papers published from 2012 to 2016 is suggested.
-2. If a paper is added to the list, another paper (usually from 
-More Papers from 2016" section) should be removed to keep top 100 papers. (Thus, removing papers is also important contributions as well as adding papers)
+2. If a paper is added to the list, another paper (usually from More Papers from 2016" section) should be removed to keep top 100 papers. (Thus, removing papers is also important contributions as well as adding papers)
 3. Papers that are important, but failed to be included in the list, will be listed in More than Top 100 section.
 4. Please refer to New Papers and Old Papers sections for the papers published in recent 6 months or before 2012.
 
@@ -37,18 +36,17 @@
 - 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?
 
@@ -93,8 +91,7 @@
 Optimization / Training Techniques
 - Training very deep networks (2015), R. Srivastava et al. pdf  (http://papers.nips.cc/paper/5850-training-very-deep-networks.pdf)
 - Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015), S. Loffe and C. Szegedy pdf  (http://arxiv.org/pdf/1502.03167)
-- Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), K. He et al. pdf  
-(http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf)
+- Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), K. He et al. pdf  (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf)
 - Dropout: A simple way to prevent neural networks from overfitting (2014), N. Srivastava et al. pdf  (http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)
 - Adam: A method for stochastic optimization (2014), D. Kingma and J. Ba pdf  (http://arxiv.org/pdf/1412.6980)
 - Improving neural networks by preventing co-adaptation of feature detectors (2012), G. Hinton et al. pdf  (http://arxiv.org/pdf/1207.0580.pdf)
@@ -131,11 +128,9 @@
 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)
-- 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)
+- 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)
-- Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al. pdf  
-(http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf)
+- Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf)
 - Spatial pyramid pooling in deep convolutional networks for visual recognition (2014), K. He et al. pdf  (http://arxiv.org/pdf/1406.4729)
 - Semantic image segmentation with deep convolutional nets and fully connected CRFs, L. Chen et al. pdf  (https://arxiv.org/pdf/1412.7062)
 - 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)
@@ -145,15 +140,13 @@
 Image / Video / Etc
 - Image Super-Resolution Using Deep Convolutional Networks (2016), C. Dong et al. pdf  (https://arxiv.org/pdf/1501.00092v3.pdf)
 - A neural algorithm of artistic style (2015), L. Gatys et al. pdf  (https://arxiv.org/pdf/1508.06576)
-- 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)
+- 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)
 - 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):
+- 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)
 - 3D convolutional neural networks for human action recognition (2013), S. Ji et al. pdf  (http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_JiXYY10.pdf)
@@ -167,8 +160,7 @@
 - Exploring the limits of language modeling (2016), R. Jozefowicz et al. pdf  (http://arxiv.org/pdf/1602.02410)
 - Teaching machines to read and comprehend (2015), K. Hermann et al. pdf  (http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf)
 - Effective approaches to attention-based neural machine translation (2015), M. Luong et al. pdf  (https://arxiv.org/pdf/1508.04025)
-- Conditional random fields as recurrent neural networks (2015), S. Zheng and S. Jayasumana. pdf  
-(http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Conditional_Random_Fields_ICCV_2015_paper.pdf)
+- Conditional random fields as recurrent neural networks (2015), S. Zheng and S. Jayasumana. pdf  (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Conditional_Random_Fields_ICCV_2015_paper.pdf)
 - Memory networks (2014), J. Weston et al. pdf  (https://arxiv.org/pdf/1410.3916)
 - Neural turing machines (2014), A. Graves et al. pdf  (https://arxiv.org/pdf/1410.5401)
 - Neural machine translation by jointly learning to align and translate (2014), D. Bahdanau et al. pdf  (http://arxiv.org/pdf/1409.0473)
@@ -178,8 +170,7 @@
 - Convolutional neural networks for sentence classification (2014), Y. Kim pdf  (http://arxiv.org/pdf/1408.5882)
 - Glove: Global vectors for word representation (2014), J. Pennington et al. pdf  (http://anthology.aclweb.org/D/D14/D14-1162.pdf)
 - Distributed representations of sentences and documents (2014), Q. Le and T. Mikolov pdf  (http://arxiv.org/pdf/1405.4053)
-- 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)
+- 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)
 - Generating sequences with recurrent neural networks (2013), A. Graves. pdf  (https://arxiv.org/pdf/1308.0850)
@@ -255,16 +246,13 @@
 - Deep sparse rectifier neural networks (2011), X. Glorot et al. pdf  (http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_GlorotBB11.pdf)
 - Natural language processing (almost) from scratch (2011), R. Collobert et al. pdf  (http://arxiv.org/pdf/1103.0398)
 - Recurrent neural network based language model (2010), T. Mikolov et al. pdf  (http://www.fit.vutbr.cz/research/groups/speech/servite/2010/rnnlm_mikolov.pdf)
-- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. pdf  
-(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484&rep=rep1&type=pdf)
+- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. pdf  (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)
 - 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)
+- 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)
 - 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)
@@ -336,28 +324,23 @@
 - Achieving open vocabulary neural machine translation with hybrid word-character models, M. Luong and C. Manning. pdf  (https://arxiv.org/pdf/1604.00788)
 - Very Deep Convolutional Networks for Natural Language Processing (2016), A. Conneau et al. pdf  (https://arxiv.org/pdf/1606.01781)
 - Bag of tricks for efficient text classification (2016), A. Joulin et al. pdf  (https://arxiv.org/pdf/1607.01759)
-- Efficient piecewise training of deep structured models for semantic segmentation (2016), G. Lin et al. pdf  
-(http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lin_Efficient_Piecewise_Training_CVPR_2016_paper.pdf)
+- Efficient piecewise training of deep structured models for semantic segmentation (2016), G. Lin et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lin_Efficient_Piecewise_Training_CVPR_2016_paper.pdf)
 - Learning to compose neural networks for question answering (2016), J. Andreas et al. pdf  (https://arxiv.org/pdf/1601.01705)
 - Perceptual losses for real-time style transfer and super-resolution (2016), J. Johnson et al. pdf  (https://arxiv.org/pdf/1603.08155)
 - Reading text in the wild with convolutional neural networks (2016), M. Jaderberg et al. pdf  (http://arxiv.org/pdf/1412.1842)
 - What makes for effective detection proposals? (2016), J. Hosang et al. pdf  (https://arxiv.org/pdf/1502.05082)
 - Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks (2016), S. Bell et al. pdf  
 (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)
+- 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)
 - 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)
 
 (2015)
-- Ask your neurons: A neural-based approach to answering questions about images (2015), M. Malinowski et al. pdf  
-(http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Malinowski_Ask_Your_Neurons_ICCV_2015_paper.pdf)
+- Ask your neurons: A neural-based approach to answering questions about images (2015), M. Malinowski et al. pdf  (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Malinowski_Ask_Your_Neurons_ICCV_2015_paper.pdf)
 - Exploring models and data for image question answering (2015), M. Ren et al. pdf  (http://papers.nips.cc/paper/5640-stochastic-variational-inference-for-hidden-markov-models.pdf)
-- Are you talking to a machine? dataset and methods for multilingual image question (2015), H. Gao et al. pdf  
-(http://papers.nips.cc/paper/5641-are-you-talking-to-a-machine-dataset-and-methods-for-multilingual-image-question.pdf)
-- Mind's eye: A recurrent visual representation for image caption generation (2015), X. Chen and C. Zitnick. pdf  
-(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Chen_Minds_Eye_A_2015_CVPR_paper.pdf)
+- Are you talking to a machine? dataset and methods for multilingual image question (2015), H. Gao et al. pdf  (http://papers.nips.cc/paper/5641-are-you-talking-to-a-machine-dataset-and-methods-for-multilingual-image-question.pdf)
+- Mind's eye: A recurrent visual representation for image caption generation (2015), X. Chen and C. Zitnick. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Chen_Minds_Eye_A_2015_CVPR_paper.pdf)
 - From captions to visual concepts and back (2015), H. Fang et al. pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Fang_From_Captions_to_2015_CVPR_paper.pdf).
 - Towards AI-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al. pdf  (http://arxiv.org/pdf/1502.05698)
 - Ask me anything: Dynamic memory networks for natural language processing (2015), A. Kumar et al. pdf  (http://arxiv.org/pdf/1506.07285)
@@ -369,15 +352,12 @@
 - Trust Region Policy Optimization (2015), J. Schulman et al. pdf  (http://www.jmlr.org/proceedings/papers/v37/schulman15.pdf)
 - Beyond short snippents: Deep networks for video classification (2015) pdf  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ng_Beyond_Short_Snippets_2015_CVPR_paper.pdf)
 - Learning Deconvolution Network for Semantic Segmentation (2015), H. Noh et al. pdf  (https://arxiv.org/pdf/1505.04366v1)
-- Learning spatiotemporal features with 3d convolutional networks (2015), D. Tran et al. pdf  
-(http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Tran_Learning_Spatiotemporal_Features_ICCV_2015_paper.pdf)
+- Learning spatiotemporal features with 3d convolutional networks (2015), D. Tran et al. pdf  (http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Tran_Learning_Spatiotemporal_Features_ICCV_2015_paper.pdf)
 - Understanding neural networks through deep visualization (2015), J. Yosinski et al. pdf  (https://arxiv.org/pdf/1506.06579)
 - An Empirical Exploration of Recurrent Network Architectures (2015), R. Jozefowicz et al.  pdf  (http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
-- Deep generative image models using a laplacian pyramid of adversarial networks (2015), E.Denton et al. pdf  
-(http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf)
+- Deep generative image models using a laplacian pyramid of adversarial networks (2015), E.Denton et al. pdf  (http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks.pdf)
 - Gated Feedback Recurrent Neural Networks (2015), J. Chung et al. pdf  (http://www.jmlr.org/proceedings/papers/v37/chung15.pdf)
-- Fast and accurate deep network learning by exponential linear units (ELUS) (2015), D. Clevert et al. pdf  
-(https://arxiv.org/pdf/1511.07289.pdf%5Cnhttp://arxiv.org/abs/1511.07289%5Cnhttp://arxiv.org/abs/1511.07289)
+- Fast and accurate deep network learning by exponential linear units (ELUS) (2015), D. Clevert et al. pdf  (https://arxiv.org/pdf/1511.07289.pdf%5Cnhttp://arxiv.org/abs/1511.07289%5Cnhttp://arxiv.org/abs/1511.07289)
 - Pointer networks (2015), O. Vinyals et al. pdf  (http://papers.nips.cc/paper/5866-pointer-networks.pdf)
 - Visualizing and Understanding Recurrent Networks (2015), A. Karpathy et al. pdf  (https://arxiv.org/pdf/1506.02078)
 - Attention-based models for speech recognition (2015), J. Chorowski et al. pdf  (http://papers.nips.cc/paper/5847-attention-based-models-for-speech-recognition.pdf)