awesome-deep-learning-papers

Deep learning papers

A curated list of the most cited deep learning papers from 2012 to 2016, serving as a starting point for understanding deep learning research.

The most cited deep learning papers

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deep-learningdeep-neural-networksmachine-learning

Awesome - Most Cited Deep Learning Papers / Contents / Understanding / Generalization / Transfer
[pdf] (2015), G. Hinton et al
[pdf] (2015), A. Nguyen et al
[pdf] (2014), J. Yosinski et al
[pdf] (2014), A. Razavian et al
[pdf] (2014), M. Oquab et al
[pdf] (2014), M. Zeiler and R. Fergus
[pdf] (2014), J. Donahue et al

Awesome - Most Cited Deep Learning Papers / Contents / Optimization / Training Techniques
[pdf] (2015), R. Srivastava et al
[pdf] (2015), S. Loffe and C. Szegedy
[pdf] (2015), K. He et al
[pdf] (2014), N. Srivastava et al
[pdf] (2014), D. Kingma and J. Ba
[pdf] (2012), G. Hinton et al
[pdf] (2012) J. Bergstra and Y. Bengio

Awesome - Most Cited Deep Learning Papers / Contents / Unsupervised / Generative Models
[pdf] (2016), A. Oord et al
[pdf] (2016), T. Salimans et al
[pdf] (2015), A. Radford et al
[pdf] (2015), K. Gregor et al
[pdf] (2014), I. Goodfellow et al
[pdf] (2013), D. Kingma and M. Welling
[pdf] (2013), Q. Le et al

Awesome - Most Cited Deep Learning Papers / Contents / Convolutional Neural Network Models
[pdf] (2016), C. Szegedy et al
[pdf] (2016), C. Szegedy et al
[pdf] (2016), K. He et al
[pdf] (2016), K. He et al
[pdf] (2015), M. Jaderberg et al.,
[pdf] (2015), C. Szegedy et al
[pdf] (2014), K. Simonyan and A. Zisserman
[pdf] (2014), K. Chatfield et al
[pdf] (2013), P. Sermanet et al
[pdf] (2013), I. Goodfellow et al
[pdf] (2013), M. Lin et al
[pdf] (2012), A. Krizhevsky et al

Awesome - Most Cited Deep Learning Papers / Contents / Image: Segmentation / Object Detection
[pdf] (2016), J. Redmon et al
[pdf] (2015), J. Long et al
[pdf] (2015), S. Ren et al
[pdf] (2015), R. Girshick
[pdf] (2014), R. Girshick et al
[pdf] (2014), K. He et al
[pdf] , L. Chen et al
[pdf] (2013), C. Farabet et al

Awesome - Most Cited Deep Learning Papers / Contents / Image / Video / Etc
[pdf] (2016), C. Dong et al
[pdf] (2015), L. Gatys et al
[pdf] (2015), A. Karpathy and L. Fei-Fei
[pdf] (2015), K. Xu et al
[pdf] (2015), O. Vinyals et al
[pdf] (2015), J. Donahue et al
[pdf] (2015), S. Antol et al
[pdf] (2014), Y. Taigman et al. :
[pdf] (2014), A. Karpathy et al
[pdf] (2014), K. Simonyan et al
[pdf] (2013), S. Ji et al

Awesome - Most Cited Deep Learning Papers / Contents / Natural Language Processing / RNNs
[pdf] (2016), G. Lample et al
[pdf] (2016), R. Jozefowicz et al
[pdf] (2015), K. Hermann et al
[pdf] (2015), M. Luong et al
[pdf] (2015), S. Zheng and S. Jayasumana
[pdf] (2014), J. Weston et al
[pdf] (2014), A. Graves et al
[pdf] (2014), D. Bahdanau et al
[pdf] (2014), I. Sutskever et al
[pdf] (2014), K. Cho et al
[pdf] (2014), N. Kalchbrenner et al
[pdf] (2014), Y. Kim
[pdf] (2014), J. Pennington et al
[pdf] (2014), Q. Le and T. Mikolov
[pdf] (2013), T. Mikolov et al
[pdf] (2013), T. Mikolov et al
[pdf] (2013), R. Socher et al
[pdf] (2013), A. Graves

Awesome - Most Cited Deep Learning Papers / Contents / Speech / Other Domain
[pdf] (2016), D. Bahdanau et al
[pdf] (2015), D. Amodei et al
[pdf] (2013), A. Graves
[pdf] (2012), G. Hinton et al
[pdf] (2012) G. Dahl et al
[pdf] (2012), A. Mohamed et al

Awesome - Most Cited Deep Learning Papers / Contents / Reinforcement Learning / Robotics
[pdf] (2016), S. Levine et al
[pdf] (2016), S. Levine et al
[pdf] (2016), V. Mnih et al
[pdf] (2016), H. Hasselt et al
[pdf] (2016), D. Silver et al
[pdf] (2015), T. Lillicrap et al
[pdf] (2015), V. Mnih et al
[pdf] (2015), I. Lenz et al
[pdf] (2013), V. Mnih et al. )

Awesome - Most Cited Deep Learning Papers / Contents / More Papers from 2016
[pdf] (2016), J. Ba et al
[pdf] (2016), M. Andrychowicz et al
[pdf] (2016), Y. Ganin et al
[pdf] (2016), A. Oord et al
[pdf] (2016), R. Zhang et al
[pdf] (2016), J. Zhu et al
[pdf] (2016), D Ulyanov et al
[pdf] (2016), W. Liu et al
[pdf] (2016), F. Iandola et al
[pdf] (2016), S. Han et al
[pdf] (2016), M. Courbariaux et al
[pdf] (2016), C. Xiong et al
[pdf] (2016), Z. Yang et al
[pdf] (2016), A. Graves et al
[pdf] (2016), Y. Wu et al

Awesome - Most Cited Deep Learning Papers / Contents / New papers
[pdf] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017), Andrew G. Howard et al
[pdf] Convolutional Sequence to Sequence Learning (2017), Jonas Gehring et al
[pdf] A Knowledge-Grounded Neural Conversation Model (2017), Marjan Ghazvininejad et al
[pdf] Accurate, Large Minibatch SGD:Training ImageNet in 1 Hour (2017), Priya Goyal et al
[pdf] TACOTRON: Towards end-to-end speech synthesis (2017), Y. Wang et al
[pdf] Deep Photo Style Transfer (2017), F. Luan et al
[pdf] Evolution Strategies as a Scalable Alternative to Reinforcement Learning (2017), T. Salimans et al
[pdf] Deformable Convolutional Networks (2017), J. Dai et al
[pdf] Mask R-CNN (2017), K. He et al
[pdf] Learning to discover cross-domain relations with generative adversarial networks (2017), T. Kim et al
[pdf] Deep voice: Real-time neural text-to-speech (2017), S. Arik et al.,
[pdf] PixelNet: Representation of the pixels, by the pixels, and for the pixels (2017), A. Bansal et al
[pdf] Batch renormalization: Towards reducing minibatch dependence in batch-normalized models (2017), S. Ioffe
[pdf] Wasserstein GAN (2017), M. Arjovsky et al
[pdf] Understanding deep learning requires rethinking generalization (2017), C. Zhang et al
[pdf] Least squares generative adversarial networks (2016), X. Mao et al

Awesome - Most Cited Deep Learning Papers / Contents / Old Papers
[pdf] An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al
[pdf] Deep sparse rectifier neural networks (2011), X. Glorot et al
[pdf] Natural language processing (almost) from scratch (2011), R. Collobert et al
[pdf] Recurrent neural network based language model (2010), T. Mikolov et al
[pdf] Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al
[pdf] Learning mid-level features for recognition (2010), Y. Boureau
[pdf] A practical guide to training restricted boltzmann machines (2010), G. Hinton
[pdf] Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio
[pdf] Why does unsupervised pre-training help deep learning (2010), D. Erhan et al
[pdf] Learning deep architectures for AI (2009), Y. Bengio
[pdf] Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (2009), H. Lee et al
[pdf] Greedy layer-wise training of deep networks (2007), Y. Bengio et al
[pdf] Reducing the dimensionality of data with neural networks, G. Hinton and R. Salakhutdinov
[pdf] A fast learning algorithm for deep belief nets (2006), G. Hinton et al
[pdf] Gradient-based learning applied to document recognition (1998), Y. LeCun et al
[pdf] Long short-term memory (1997), S. Hochreiter and J. Schmidhuber

Awesome - Most Cited Deep Learning Papers / Contents / HW / SW / Dataset
[pdf] SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016), Rajpurkar et al
[pdf] OpenAI gym (2016), G. Brockman et al
[pdf] TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016), M. Abadi et al
[pdf] Torch7: A matlab-like environment for machine learning, R. Collobert et al
[pdf] MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc
[pdf] Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al
[pdf] Caffe: Convolutional architecture for fast feature embedding (2014), Y. Jia et al

Awesome - Most Cited Deep Learning Papers / Contents / Book / Survey / Review
[pdf] On the Origin of Deep Learning (2017), H. Wang and Bhiksha Raj
[pdf] Deep Reinforcement Learning: An Overview (2017), Y. Li,
[pdf] Neural Machine Translation and Sequence-to-sequence Models(2017): A Tutorial, G. Neubig
[html] Neural Network and Deep Learning (Book, Jan 2017), Michael Nielsen
[html] Deep learning (Book, 2016), Goodfellow et al
[pdf] LSTM: A search space odyssey (2016), K. Greff et al
[pdf] Tutorial on Variational Autoencoders (2016), C. Doersch
[pdf] Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton
[pdf] Deep learning in neural networks: An overview (2015), J. Schmidhuber
[pdf] Representation learning: A review and new perspectives (2013), Y. Bengio et al

Awesome - Most Cited Deep Learning Papers / Contents / Video Lectures / Tutorials / Blogs
[web] CS231n, Convolutional Neural Networks for Visual Recognition, Stanford University
[web] CS224d, Deep Learning for Natural Language Processing, Stanford University
[web] 15,683 over 1 year ago Oxford Deep NLP 2017, Deep Learning for Natural Language Processing, University of Oxford
[web] NIPS 2016 Tutorials, Long Beach
[web] ICML 2016 Tutorials, New York City
[web] ICLR 2016 Videos, San Juan
[web] Deep Learning Summer School 2016, Montreal
[web] Bay Area Deep Learning School 2016, Stanford
[web] OpenAI
[web] Distill
[web] Andrej Karpathy Blog
[Web] Colah's Blog
[Web] WildML
[web] FastML
[web] TheMorningPaper

Awesome - Most Cited Deep Learning Papers / Contents / Appendix: More than Top 100
[pdf] A character-level decoder without explicit segmentation for neural machine translation (2016), J. Chung et al
[html] Dermatologist-level classification of skin cancer with deep neural networks (2017), A. Esteva et al
[pdf] Weakly supervised object localization with multi-fold multiple instance learning (2017), R. Gokberk et al
[pdf] Brain tumor segmentation with deep neural networks (2017), M. Havaei et al
[pdf] Professor Forcing: A New Algorithm for Training Recurrent Networks (2016), A. Lamb et al
[web] Adversarially learned inference (2016), V. Dumoulin et al
[pdf] Understanding convolutional neural networks (2016), J. Koushik
[pdf] Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al
[pdf] Adaptive computation time for recurrent neural networks (2016), A. Graves
[pdf] Densely connected convolutional networks (2016), G. Huang et al
[pdf] Continuous deep q-learning with model-based acceleration (2016), S. Gu et al
[pdf] A thorough examination of the cnn/daily mail reading comprehension task (2016), D. Chen et al
[pdf] Achieving open vocabulary neural machine translation with hybrid word-character models, M. Luong and C. Manning
[pdf] Very Deep Convolutional Networks for Natural Language Processing (2016), A. Conneau et al
[pdf] Bag of tricks for efficient text classification (2016), A. Joulin et al
[pdf] Efficient piecewise training of deep structured models for semantic segmentation (2016), G. Lin et al
[pdf] Learning to compose neural networks for question answering (2016), J. Andreas et al
[pdf] Perceptual losses for real-time style transfer and super-resolution (2016), J. Johnson et al
[pdf] Reading text in the wild with convolutional neural networks (2016), M. Jaderberg et al
[pdf] What makes for effective detection proposals? (2016), J. Hosang et al
[pdf] Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks (2016), S. Bell et al.
[pdf] Instance-aware semantic segmentation via multi-task network cascades (2016), J. Dai et al
[pdf] Conditional image generation with pixelcnn decoders (2016), A. van den Oord et al
[pdf] Deep networks with stochastic depth (2016), G. Huang et al.,
[pdf] Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics (2016), Yee Whye Teh et al
[pdf] Ask your neurons: A neural-based approach to answering questions about images (2015), M. Malinowski et al
[pdf] Exploring models and data for image question answering (2015), M. Ren et al
[pdf] Are you talking to a machine? dataset and methods for multilingual image question (2015), H. Gao et al
[pdf] Mind's eye: A recurrent visual representation for image caption generation (2015), X. Chen and C. Zitnick
[pdf] From captions to visual concepts and back (2015), H. Fang et al.
[pdf] Towards AI-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al
[pdf] Ask me anything: Dynamic memory networks for natural language processing (2015), A. Kumar et al
[pdf] Unsupervised learning of video representations using LSTMs (2015), N. Srivastava et al
[pdf] Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding (2015), S. Han et al
[pdf] Improved semantic representations from tree-structured long short-term memory networks (2015), K. Tai et al
[pdf] Character-aware neural language models (2015), Y. Kim et al
[pdf] Grammar as a foreign language (2015), O. Vinyals et al
[pdf] Trust Region Policy Optimization (2015), J. Schulman et al
[pdf] Beyond short snippents: Deep networks for video classification (2015)
[pdf] Learning Deconvolution Network for Semantic Segmentation (2015), H. Noh et al
[pdf] Learning spatiotemporal features with 3d convolutional networks (2015), D. Tran et al
[pdf] Understanding neural networks through deep visualization (2015), J. Yosinski et al
[pdf] An Empirical Exploration of Recurrent Network Architectures (2015), R. Jozefowicz et al
[pdf] Deep generative image models using a laplacian pyramid of adversarial networks (2015), E.Denton et al
[pdf] Gated Feedback Recurrent Neural Networks (2015), J. Chung et al
[pdf] Fast and accurate deep network learning by exponential linear units (ELUS) (2015), D. Clevert et al
[pdf] Pointer networks (2015), O. Vinyals et al
[pdf] Visualizing and Understanding Recurrent Networks (2015), A. Karpathy et al
[pdf] Attention-based models for speech recognition (2015), J. Chorowski et al
[pdf] End-to-end memory networks (2015), S. Sukbaatar et al
[pdf] Describing videos by exploiting temporal structure (2015), L. Yao et al
[pdf] A neural conversational model (2015), O. Vinyals and Q. Le
https://www.transacl.org/ojs/index.php/tacl/article/download/570/124 Improving distributional similarity with lessons learned from word embeddings, O. Levy et al. [[pdf]] ( )
[pdf] Transition-Based Dependency Parsing with Stack Long Short-Term Memory (2015), C. Dyer et al
[pdf] Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs (2015), M. Ballesteros et al
[pdf] Finding function in form: Compositional character models for open vocabulary word representation (2015), W. Ling et al
[pdf] DeepPose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy
[pdf] Learning a Deep Convolutional Network for Image Super-Resolution (2014, C. Dong et al
[pdf] Recurrent models of visual attention (2014), V. Mnih et al
[pdf] Empirical evaluation of gated recurrent neural networks on sequence modeling (2014), J. Chung et al
[pdf] Addressing the rare word problem in neural machine translation (2014), M. Luong et al
[pdf] Recurrent neural network regularization (2014), W. Zaremba et al
[pdf] Intriguing properties of neural networks (2014), C. Szegedy et al
[pdf] Towards end-to-end speech recognition with recurrent neural networks (2014), A. Graves and N. Jaitly
[pdf] Scalable object detection using deep neural networks (2014), D. Erhan et al
[pdf] On the importance of initialization and momentum in deep learning (2013), I. Sutskever et al
[pdf] Regularization of neural networks using dropconnect (2013), L. Wan et al
[pdf] Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al
[pdf] Linguistic Regularities in Continuous Space Word Representations (2013), T. Mikolov et al
[pdf] Large scale distributed deep networks (2012), J. Dean et al
[pdf] A Fast and Accurate Dependency Parser using Neural Networks. Chen and Manning

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