Understanding / Generalization / Transfer
Optimization / Training Techniques
Unsupervised / Generative Models
Image Segmentation / Object Detection
Natural Language Processing / RNNs
Reinforcement Learning / Robotics
(More than Top 100)
New Papers: Less than 6 months
Old Papers: Before 2012
HW / SW / Dataset: Technical reports
Video Lectures / Tutorials / Blogs
Appendix: More than Top 100: More papers not in the list
Understanding / Generalization / Transfer
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
Training very deep networks(2015), R. Srivastava et al.[pdf]
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]
Convolutional Neural Network Models
Rethinking the inception architecture for computer vision(2016), C. Szegedy et al.[pdf]
Inception-v4, inception-resnet and the impact of residual connections on learning(2016), C. Szegedy et al.[pdf]
Identity Mappings in Deep Residual Networks(2016), K. He et al.[pdf]
Deep residual learning for image recognition(2016), K. He et al.[pdf]
Spatial transformer network(2015), M. Jaderberg et al.,[pdf]
Going deeper with convolutions(2015), C. Szegedy et al.[pdf]
Very deep convolutional networks for large-scale image recognition(2014), K. Simonyan and A. Zisserman[pdf]
Return of the devil in the details: delving deep into convolutional nets(2014), K. Chatfield et al.[pdf]
OverFeat: Integrated recognition, localization and detection using convolutional networks(2013), P. Sermanet et al.[pdf]
Maxout networks(2013), I. Goodfellow et al.[pdf]
Network in network(2013), M. Lin et al.[pdf]
ImageNet classification with deep convolutional neural networks(2012), A. Krizhevsky 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]
Spatial pyramid pooling in deep convolutional networks for visual recognition(2014), K. He 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 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]
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 / RNNs
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]
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 / Robotics
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])
Layer Normalization(2016), J. Ba et al.[pdf]
Learning to learn by gradient descent by gradient descent(2016), M. Andrychowicz et al.[pdf]
Domain-adversarial training of neural networks(2016), Y. Ganin et al.[pdf]
WaveNet: A Generative Model for Raw Audio(2016), A. Oord et al.[pdf][web]
Colorful image colorization(2016), R. Zhang et al.[pdf]
Generative visual manipulation on the natural image manifold(2016), J. Zhu et al.[pdf]
Texture networks: Feed-forward synthesis of textures and stylized images(2016), D Ulyanov et al.[pdf]
SSD: Single shot multibox detector(2016), W. Liu et al.[pdf]
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size(2016), F. Iandola et al.[pdf]
Eie: Efficient inference engine on compressed deep neural network(2016), S. Han et al.[pdf]
Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1(2016), M. Courbariaux et al.[pdf]
Dynamic memory networks for visual and textual question answering(2016), C. Xiong et al.[pdf]
Stacked attention networks for image question answering(2016), Z. Yang et al.[pdf]
Hybrid computing using a neural network with dynamic external memory(2016), A. Graves et al.[pdf]
Google's neural machine translation system: Bridging the gap between human and machine translation(2016), Y. Wu et al.[pdf]
Newly published papers (< 6 months) which are worth reading
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.[pdf]
Classic papers published before 2012
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.[pdf]
OpenAI gym (2016), G. Brockman et al.[pdf]
TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016), M. Abadi et al.[pdf]
Theano: A Python framework for fast computation of mathematical expressions, R. Al-Rfou et al.
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.[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.[pdf]
Neural Network and Deep Learning (Book, Jan 2017), Michael Nielsen.[html]
Deep learning (Book, 2016), Goodfellow et al.[html]
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.[pdf]
Video Lectures / Tutorials / Blogs
(Lectures)
CS231n, Convolutional Neural Networks for Visual Recognition, Stanford University[web]
CS224d, Deep Learning for Natural Language Processing, Stanford University[web]
Oxford Deep NLP 2017, Deep Learning for Natural Language Processing, University of Oxford[web]
(Tutorials)
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]
(Blogs)
OpenAI[web]
Distill[web]
Andrej Karpathy Blog[web]
Colah's Blog[Web]
WildML[Web]
FastML[web]
TheMorningPaper[web]
(2016)
A character-level decoder without explicit segmentation for neural machine translation (2016), J. Chung et al.[pdf]
Dermatologist-level classification of skin cancer with deep neural networks (2017), A. Esteva et al.[html]
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.[pdf]
Adversarially learned inference (2016), V. Dumoulin et al.[web][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]
Region-based convolutional networks for accurate object detection and segmentation (2016), R. Girshick et al.
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]
(2015)
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.[pdf]
Improving distributional similarity with lessons learned from word embeddings, O. Levy et al. [[pdf]] (https://www.transacl.org/ojs/index.php/tacl/article/download/570/124)
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]
(~2014)
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]
On the properties of neural machine translation: Encoder-decoder approaches (2014), K. Cho et. al.
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.[pdf]
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