awesome-deep-learning-papers

(2015), G. Hinton et al

(2015), A. Nguyen et al

(2014), J. Yosinski et al

(2014), A. Razavian et al

(2014), M. Oquab et al

(2014), M. Zeiler and R. Fergus

(2014), J. Donahue et al

(2015), R. Srivastava et al

(2015), S. Loffe and C. Szegedy

(2015), K. He et al

(2014), N. Srivastava et al

(2014), D. Kingma and J. Ba

(2012), G. Hinton et al

(2012) J. Bergstra and Y. Bengio

(2016), A. Oord et al

(2016), T. Salimans et al

(2015), A. Radford et al

(2015), K. Gregor et al

(2014), I. Goodfellow et al

(2013), D. Kingma and M. Welling

(2013), Q. Le et al

(2016), C. Szegedy et al

(2016), C. Szegedy et al

(2016), K. He et al

(2016), K. He et al

(2015), M. Jaderberg et al.,

(2015), C. Szegedy et al

(2014), K. Simonyan and A. Zisserman

(2014), K. Chatfield et al

(2013), P. Sermanet et al

(2013), I. Goodfellow et al

(2013), M. Lin et al

(2012), A. Krizhevsky et al

(2016), J. Redmon et al

(2015), J. Long et al

(2015), S. Ren et al

(2015), R. Girshick

(2014), R. Girshick et al

(2014), K. He et al

, L. Chen et al

(2013), C. Farabet et al

(2016), C. Dong et al

(2015), L. Gatys et al

(2015), A. Karpathy and L. Fei-Fei

(2015), K. Xu et al

(2015), O. Vinyals et al

(2015), J. Donahue et al

(2015), S. Antol et al

(2014), Y. Taigman et al. :

(2014), A. Karpathy et al

(2014), K. Simonyan et al

(2013), S. Ji et al

(2016), G. Lample et al

(2016), R. Jozefowicz et al

(2015), K. Hermann et al

(2015), M. Luong et al

(2015), S. Zheng and S. Jayasumana

(2014), J. Weston et al

(2014), A. Graves et al

(2014), D. Bahdanau et al

(2014), I. Sutskever et al

(2014), K. Cho et al

(2014), N. Kalchbrenner et al

(2014), Y. Kim

(2014), J. Pennington et al

(2014), Q. Le and T. Mikolov

(2013), T. Mikolov et al

(2013), T. Mikolov et al

(2013), R. Socher et al

(2013), A. Graves

(2016), D. Bahdanau et al

(2015), D. Amodei et al

(2013), A. Graves

(2012), G. Hinton et al

(2012) G. Dahl et al

(2012), A. Mohamed et al

(2016), S. Levine et al

(2016), S. Levine et al

(2016), V. Mnih et al

(2016), H. Hasselt et al

(2016), D. Silver et al

(2015), T. Lillicrap et al

(2015), V. Mnih et al

(2015), I. Lenz et al

(2013), V. Mnih et al. )

(2016), J. Ba et al

(2016), M. Andrychowicz et al

(2016), Y. Ganin et al

(2016), A. Oord et al

(2016), R. Zhang et al

(2016), J. Zhu et al

(2016), D Ulyanov et al

(2016), W. Liu et al

(2016), F. Iandola et al

(2016), S. Han et al

(2016), M. Courbariaux et al

(2016), C. Xiong et al

(2016), Z. Yang et al

(2016), A. Graves et al

(2016), Y. Wu et al

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017), Andrew G. Howard et al

Convolutional Sequence to Sequence Learning (2017), Jonas Gehring et al

A Knowledge-Grounded Neural Conversation Model (2017), Marjan Ghazvininejad et al

Accurate, Large Minibatch SGD:Training ImageNet in 1 Hour (2017), Priya Goyal et al

TACOTRON: Towards end-to-end speech synthesis (2017), Y. Wang et al

Deep Photo Style Transfer (2017), F. Luan et al

Evolution Strategies as a Scalable Alternative to Reinforcement Learning (2017), T. Salimans et al

Deformable Convolutional Networks (2017), J. Dai et al

Mask R-CNN (2017), K. He et al

Learning to discover cross-domain relations with generative adversarial networks (2017), T. Kim et al

Deep voice: Real-time neural text-to-speech (2017), S. Arik et al.,

PixelNet: Representation of the pixels, by the pixels, and for the pixels (2017), A. Bansal et al

Batch renormalization: Towards reducing minibatch dependence in batch-normalized models (2017), S. Ioffe

Wasserstein GAN (2017), M. Arjovsky et al

Understanding deep learning requires rethinking generalization (2017), C. Zhang et al

Least squares generative adversarial networks (2016), X. Mao et al

An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al

Deep sparse rectifier neural networks (2011), X. Glorot et al

Natural language processing (almost) from scratch (2011), R. Collobert et al

Recurrent neural network based language model (2010), T. Mikolov et al

Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al

Learning mid-level features for recognition (2010), Y. Boureau

A practical guide to training restricted boltzmann machines (2010), G. Hinton

Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio

Why does unsupervised pre-training help deep learning (2010), D. Erhan et al

Learning deep architectures for AI (2009), Y. Bengio

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (2009), H. Lee et al

Greedy layer-wise training of deep networks (2007), Y. Bengio et al

Reducing the dimensionality of data with neural networks, G. Hinton and R. Salakhutdinov

A fast learning algorithm for deep belief nets (2006), G. Hinton et al

Gradient-based learning applied to document recognition (1998), Y. LeCun et al

Long short-term memory (1997), S. Hochreiter and J. Schmidhuber

SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016), Rajpurkar et al

OpenAI gym (2016), G. Brockman et al

TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016), M. Abadi et al

Torch7: A matlab-like environment for machine learning, R. Collobert et al

MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc

Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al

Caffe: Convolutional architecture for fast feature embedding (2014), Y. Jia et al

On the Origin of Deep Learning (2017), H. Wang and Bhiksha Raj

Deep Reinforcement Learning: An Overview (2017), Y. Li,

Neural Machine Translation and Sequence-to-sequence Models(2017): A Tutorial, G. Neubig

Neural Network and Deep Learning (Book, Jan 2017), Michael Nielsen

Deep learning (Book, 2016), Goodfellow et al

LSTM: A search space odyssey (2016), K. Greff et al

Tutorial on Variational Autoencoders (2016), C. Doersch

Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton

Deep learning in neural networks: An overview (2015), J. Schmidhuber

Representation learning: A review and new perspectives (2013), Y. Bengio et al

CS231n, Convolutional Neural Networks for Visual Recognition, Stanford University

CS224d, Deep Learning for Natural Language Processing, Stanford University

[web]

Oxford Deep NLP 2017, Deep Learning for Natural Language Processing, University of Oxford

NIPS 2016 Tutorials, Long Beach

ICML 2016 Tutorials, New York City

ICLR 2016 Videos, San Juan

Deep Learning Summer School 2016, Montreal

Bay Area Deep Learning School 2016, Stanford

OpenAI

Distill

Andrej Karpathy Blog

Colah's Blog

WildML

FastML

TheMorningPaper

A character-level decoder without explicit segmentation for neural machine translation (2016), J. Chung et al

Dermatologist-level classification of skin cancer with deep neural networks (2017), A. Esteva et al

Weakly supervised object localization with multi-fold multiple instance learning (2017), R. Gokberk et al

Brain tumor segmentation with deep neural networks (2017), M. Havaei et al

Professor Forcing: A New Algorithm for Training Recurrent Networks (2016), A. Lamb et al

Adversarially learned inference (2016), V. Dumoulin et al

Understanding convolutional neural networks (2016), J. Koushik

Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al

Adaptive computation time for recurrent neural networks (2016), A. Graves

Densely connected convolutional networks (2016), G. Huang et al

Continuous deep q-learning with model-based acceleration (2016), S. Gu et al

A thorough examination of the cnn/daily mail reading comprehension task (2016), D. Chen et al

Achieving open vocabulary neural machine translation with hybrid word-character models, M. Luong and C. Manning

Very Deep Convolutional Networks for Natural Language Processing (2016), A. Conneau et al

Bag of tricks for efficient text classification (2016), A. Joulin et al

Efficient piecewise training of deep structured models for semantic segmentation (2016), G. Lin et al

Learning to compose neural networks for question answering (2016), J. Andreas et al

Perceptual losses for real-time style transfer and super-resolution (2016), J. Johnson et al

Reading text in the wild with convolutional neural networks (2016), M. Jaderberg et al

What makes for effective detection proposals? (2016), J. Hosang et al

Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks (2016), S. Bell et al.

Instance-aware semantic segmentation via multi-task network cascades (2016), J. Dai et al

Conditional image generation with pixelcnn decoders (2016), A. van den Oord et al

Deep networks with stochastic depth (2016), G. Huang et al.,

Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics (2016), Yee Whye Teh et al

Ask your neurons: A neural-based approach to answering questions about images (2015), M. Malinowski et al

Exploring models and data for image question answering (2015), M. Ren et al

Are you talking to a machine? dataset and methods for multilingual image question (2015), H. Gao et al

Mind's eye: A recurrent visual representation for image caption generation (2015), X. Chen and C. Zitnick

From captions to visual concepts and back (2015), H. Fang et al.

Towards AI-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al

Ask me anything: Dynamic memory networks for natural language processing (2015), A. Kumar et al

Unsupervised learning of video representations using LSTMs (2015), N. Srivastava et al

Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding (2015), S. Han et al

Improved semantic representations from tree-structured long short-term memory networks (2015), K. Tai et al

Character-aware neural language models (2015), Y. Kim et al

Grammar as a foreign language (2015), O. Vinyals et al

Trust Region Policy Optimization (2015), J. Schulman et al

Beyond short snippents: Deep networks for video classification (2015)

Learning Deconvolution Network for Semantic Segmentation (2015), H. Noh et al

Learning spatiotemporal features with 3d convolutional networks (2015), D. Tran et al

Understanding neural networks through deep visualization (2015), J. Yosinski et al

An Empirical Exploration of Recurrent Network Architectures (2015), R. Jozefowicz et al

Deep generative image models using a laplacian pyramid of adversarial networks (2015), E.Denton et al

Gated Feedback Recurrent Neural Networks (2015), J. Chung et al

Fast and accurate deep network learning by exponential linear units (ELUS) (2015), D. Clevert et al

Pointer networks (2015), O. Vinyals et al

Visualizing and Understanding Recurrent Networks (2015), A. Karpathy et al

Attention-based models for speech recognition (2015), J. Chorowski et al

End-to-end memory networks (2015), S. Sukbaatar et al

Describing videos by exploiting temporal structure (2015), L. Yao et al

A neural conversational model (2015), O. Vinyals and Q. Le

Improving distributional similarity with lessons learned from word embeddings, O. Levy et al. [[pdf]] ( )

Transition-Based Dependency Parsing with Stack Long Short-Term Memory (2015), C. Dyer et al

Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs (2015), M. Ballesteros et al

Finding function in form: Compositional character models for open vocabulary word representation (2015), W. Ling et al

DeepPose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy

Learning a Deep Convolutional Network for Image Super-Resolution (2014, C. Dong et al

Recurrent models of visual attention (2014), V. Mnih et al

Empirical evaluation of gated recurrent neural networks on sequence modeling (2014), J. Chung et al

Addressing the rare word problem in neural machine translation (2014), M. Luong et al

Recurrent neural network regularization (2014), W. Zaremba et al

Intriguing properties of neural networks (2014), C. Szegedy et al

Towards end-to-end speech recognition with recurrent neural networks (2014), A. Graves and N. Jaitly

Scalable object detection using deep neural networks (2014), D. Erhan et al

On the importance of initialization and momentum in deep learning (2013), I. Sutskever et al

Regularization of neural networks using dropconnect (2013), L. Wan et al

Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al

Linguistic Regularities in Continuous Space Word Representations (2013), T. Mikolov et al

Large scale distributed deep networks (2012), J. Dean et al

A Fast and Accurate Dependency Parser using Neural Networks. Chen and Manning