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Most Cited Deep Learning Papers

本项目转载自: https://github.com/terryum/awesome-deep-learning-papers

[Notice] This list is not being maintained anymore because of the overwhelming amount of deep learning papers published every day since 2017.

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.

Contents

(More than Top 100)

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 / Video / Etc

  • 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]

Speech / Other Domain

  • 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])

More Papers from 2016

  • 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]

New papers

Newly published papers (< 6 months) which are worth reading

  • 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. [pdf]

Old Papers

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]

HW / SW / Dataset

  • 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]
  • 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]

Book / Survey / Review

  • 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)

Appendix: More than Top 100

(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]