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• Machine Learning
• Deep Learning
• Understanding
• Optimization / Training Techniques
• Unsupervised / Generative Models
• Image Segmentation / Object Detection
• Image / Video
• Natural Language Processing
• Speech / Other
• Reinforcement Learning
• New papers
• Classic Papers

Understanding

• Distilling the knowledge in a neural network(2015), G. Hinton et al. pdf
• Deep neural networks are easily fooled: High confidence predictions for unrecognizable images(2015), A. Nguyen et al. pdf
• How transferable are features in deep neural networks?(2014), J. Yosinski et al. pdf
• CNN features off-the-Shelf: An astounding baseline for recognition(2014), A. Razavian et al. pdf
• Learning and transferring mid-Level image representations using convolutional neural networks(2014), M. Oquab et al. pdf
• Visualizing and understanding convolutional networks(2014), M. Zeiler and R. Fergus pdf
• Decaf: A deep convolutional activation feature for generic visual recognition(2014), J. Donahue et al. pdf

Optimization / Training Techniques

• Batch normalization: Accelerating deep network training by reducing internal covariate shift(2015), S. Loffe and C. Szegedy pdf
• Delving deep into rectifiers: Surpassing human-level performance on imagenet classification(2015), K. He et al. pdf
• Dropout: A simple way to prevent neural networks from overfitting(2014), N. Srivastava et al. pdf
• Adam: A method for stochastic optimization(2014), D. Kingma and J. Ba pdf
• Improving neural networks by preventing co-adaptation of feature detectors(2012), G. Hinton et al. pdf
• Random search for hyper-parameter optimization(2012) J. Bergstra and Y. Bengio pdf

Unsupervised / Generative Models

• Pixel recurrent neural networks(2016), A. Oord et al. pdf
• Improved techniques for training GANs(2016), T. Salimans et al. pdf
• Unsupervised representation learning with deep convolutional generative adversarial networks(2015), A. Radford et al. pdf
• DRAW: A recurrent neural network for image generation(2015), K. Gregor et al. pdf
• Generative adversarial nets(2014), I. Goodfellow et al. pdf
• Auto-encoding variational Bayes(2013), D. Kingma and M. Welling pdf
• Building high-level features using large scale unsupervised learning(2013), Q. Le et al. pdf

Image Segmentation / Object Detection

• You only look once: Unified, real-time object detection(2016), J. Redmon et al. pdf
• Fully convolutional networks for semantic segmentation(2015), J. Long et al. pdf
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks(2015), S. Ren et al. pdf
• Fast R-CNN(2015), R. Girshick pdf
• Rich feature hierarchies for accurate object detection and semantic segmentation(2014), R. Girshick et al. pdf
• Semantic image segmentation with deep convolutional nets and fully connected CRFs, L. Chen et al. pdf
• Learning hierarchical features for scene labeling(2013), C. Farabet et al. pdf

Image / Video

• Image Super-Resolution Using Deep Convolutional Networks(2016), C. Dong et al. pdf
• A neural algorithm of artistic style(2015), L. Gatys et al. pdf
• Deep visual-semantic alignments for generating image descriptions(2015), A. Karpathy and L. Fei-Fei pdf
• Show, attend and tell: Neural image caption generation with visual attention(2015), K. Xu et al. pdf
• Show and tell: A neural image caption generator(2015), O. Vinyals et al. pdf
• Long-term recurrent convolutional networks for visual recognition and description(2015), J. Donahue et al. pdf
• VQA: Visual question answering(2015), S. Antol et al. pdf
• DeepFace: Closing the gap to human-level performance in face verification(2014), Y. Taigman et al. pdf:
• Large-scale video classification with convolutional neural networks(2014), A. Karpathy et al. pdf
• DeepPose: Human pose estimation via deep neural networks(2014), A. Toshev and C. Szegedy pdf
• Two-stream convolutional networks for action recognition in videos(2014), K. Simonyan et al. pdf
• 3D convolutional neural networks for human action recognition(2013), S. Ji et al. pdf

Natural Language Processing

• Neural Architectures for Named Entity Recognition(2016), G. Lample et al. pdf
• Exploring the limits of language modeling(2016), R. Jozefowicz et al. pdf
• Teaching machines to read and comprehend(2015), K. Hermann et al. pdf
• Effective approaches to attention-based neural machine translation(2015), M. Luong et al. pdf
• Conditional random fields as recurrent neural networks(2015), S. Zheng and S. Jayasumana. pdf
• Memory networks(2014), J. Weston et al. pdf
• Neural turing machines(2014), A. Graves et al. pdf
• Neural machine translation by jointly learning to align and translate(2014), D. Bahdanau et al. pdf
• Sequence to sequence learning with neural networks(2014), I. Sutskever et al. pdf
• Learning phrase representations using RNN encoder-decoder for statistical machine translation(2014), K. Cho et al. pdf
• A convolutional neural network for modeling sentences(2014), N. Kalchbrenner et al. pdf
• Convolutional neural networks for sentence classification(2014), Y. Kim pdf
• Glove: Global vectors for word representation(2014), J. Pennington et al. pdf
• Distributed representations of sentences and documents(2014), Q. Le and T. Mikolov pdf
• Distributed representations of words and phrases and their compositionality(2013), T. Mikolov et al. pdf
• Efficient estimation of word representations in vector space(2013), T. Mikolov et al. pdf
• Recursive deep models for semantic compositionality over a sentiment treebank(2013), R. Socher et al. pdf
• Generating sequences with recurrent neural networks(2013), A. Graves. pdf

Speech / Other

• End-to-end attention-based large vocabulary speech recognition(2016), D. Bahdanau et al. pdf
• Deep speech 2: End-to-end speech recognition in English and Mandarin(2015), D. Amodei et al. pdf
• Speech recognition with deep recurrent neural networks(2013), A. Graves pdf
• Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups(2012), G. Hinton et al. pdf
• Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition(2012) G. Dahl et al. pdf
• Acoustic modeling using deep belief networks(2012), A. Mohamed et al. pdf

Reinforcement Learning

• End-to-end training of deep visuomotor policies(2016), S. Levine et al. pdf
• Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection(2016), S. Levine et al. pdf
• Asynchronous methods for deep reinforcement learning(2016), V. Mnih et al. pdf
• Deep Reinforcement Learning with Double Q-Learning(2016), H. Hasselt et al. pdf
• Mastering the game of Go with deep neural networks and tree search(2016), D. Silver et al. pdf
• Continuous control with deep reinforcement learning(2015), T. Lillicrap et al. pdf
• Human-level control through deep reinforcement learning(2015), V. Mnih et al. pdf
• Deep learning for detecting robotic grasps(2015), I. Lenz et al. pdf
• Playing atari with deep reinforcement learning(2013), V. Mnih et al. pdf)

New papers

• 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. pdf

Classic Papers

• 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
• 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. pdf