A curated list of the most cited deep learning papers (since 2010)

I believe that there exist classic deep learning papers which are worth reading regardless of their applications. Rather than providing overwhelming amount of papers, I would like to provide a curated list of the classic deep learning papers which can be considered as must-reads in some area.

• 2016 : +30 citations (:sparkles: +50)
• 2015 : +100 citations (:sparkles: +200)
• 2014 : +200 citations (:sparkles: +400)
• 2013 : +300 citations (:sparkles: +600)
• 2012 : +400 citations (:sparkles: +800)
• 2011 : +500 citations (:sparkles: +1000)
• 2010 : +600 citations (:sparkles: +1200)

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• Survey / Review
• Theory / Future
• Optimization / Regularization
• Network Models
• Image
• Caption
• Video / Human Activity
• Word Embedding
• Machine Translation / QnA
• Speech / Etc.
• RL / Robotics
• Unsupervised
• Hardware / Software
• Papers Worth Reading
• Classic Papers
• Distinguished Researchers

Total 85 papers except for the papers in Hardware / Software, Papers Worth Reading, and Classic Papers sections.

• Deep learning (Book, 2016), Goodfellow et al. (Bengio) [html]
• 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] ✨

• 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. (Bengio) [pdf]
• Return of the devil in the details: delving deep into convolutional nets (2014), K. Chatfield et al. [pdf] ✨
• Why does unsupervised pre-training help deep learning (2010), D. Erhan et al. (Bengio) [pdf]
• Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio [pdf]

• Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015), S. Loffe and C. Szegedy (Google) [pdf] ✨
• Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), K. He et al. (Microsoft) [pdf] ✨
• Dropout: A simple way to prevent neural networks from overfitting (2014), N. Srivastava et al. (Hinton) [pdf] ✨
• Adam: A method for stochastic optimization (2014), D. Kingma and J. Ba [pdf]
• Spatial pyramid pooling in deep convolutional networks for visual recognition (2014), K. He et al. [pdf]
• On the importance of initialization and momentum in deep learning (2013), I. Sutskever et al. (Hinton) [pdf]
• Regularization of neural networks using dropconnect (2013), L. Wan et al. (LeCun) [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]

• Deep residual learning for image recognition (2016), K. He et al. (Microsoft) [pdf] ✨
• Region-based convolutional networks for accurate object detection and segmentation (2016), R. Girshick et al. (Microsoft) [pdf]
• Going deeper with convolutions (2015), C. Szegedy et al. (Google) [pdf] ✨
• Fast R-CNN (2015), R. Girshick (Microsoft) [pdf] ✨
• Fully convolutional networks for semantic segmentation (2015), J. Long et al. [pdf] ✨
• Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman [pdf] ✨
• OverFeat: Integrated recognition, localization and detection using convolutional networks (2014), P. Sermanet et al. (LeCun) [pdf]
• Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [pdf] ✨
• Maxout networks (2013), I. Goodfellow et al. (Bengio) [pdf]
• Network in network (2013), M. Lin et al. [pdf]
• ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. (Hinton) [pdf] ✨
• Large scale distributed deep networks (2012), J. Dean et al. [pdf] ✨
• Deep sparse rectifier neural networks (2011), X. Glorot et al. (Bengio) [pdf]

• Reading text in the wild with convolutional neural networks (2016), M. Jaderberg et al. (DeepMind) [pdf]
• Learning Deconvolution Network for Semantic Segmentation (2015), H. Noh et al. [pdf]
• Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al. [pdf] ✨
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. [pdf] ✨
• DRAW: A recurrent neural network for image generation (2015), K. Gregor et al. [pdf]
• Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al. [pdf] ✨
• Learning a Deep Convolutional Network for Image Super-Resolution (2014), C. Dong et al. [pdf]
• Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [pdf]
• DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014), Y. Taigman et al. (Facebook) [pdf] ✨
• Decaf: A deep convolutional activation feature for generic visual recognition (2013), J. Donahue et al. [pdf] ✨
• Learning hierarchical features for scene labeling (2013), C. Farabet et al. (LeCun) [pdf]
• Learning mid-level features for recognition (2010), Y. Boureau (LeCun) [pdf]

• Show, attend and tell: Neural image caption generation with visual attention (2015), K. Xu et al. (Bengio) [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] ✨
• Deep visual-semantic alignments for generating image descriptions (2015), A. Karpathy and L. Fei-Fei [pdf] ✨

• Large-scale video classification with convolutional neural networks (2014), A. Karpathy et al. (FeiFei) [pdf] ✨
• DeepPose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy (Google) [pdf]
• Two-stream convolutional networks for action recognition in videos (2014), K. Simonyan et al. [pdf]
• A survey on human activity recognition using wearable sensors (2013), O. Lara and M. Labrador [pdf]
• 3D convolutional neural networks for human action recognition (2013), S. Ji et al. [pdf]
• Action recognition with improved trajectories (2013), H. Wang and C. Schmid [pdf]
• Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis (2011), Q. Le et al. [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] (Google) ✨
• Distributed representations of words and phrases and their compositionality (2013), T. Mikolov et al. (Google) [pdf] ✨
• Efficient estimation of word representations in vector space (2013), T. Mikolov et al. (Google) [pdf] ✨
• Word representations: a simple and general method for semi-supervised learning (2010), J. Turian (Bengio) [pdf]

• Towards ai-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al. [pdf]
• Neural machine translation by jointly learning to align and translate (2014), D. Bahdanau et al. (Bengio) [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. (Bengio) [pdf]
• A convolutional neural network for modelling sentences (2014), N. Kalchbrenner et al. [pdf]
• Convolutional neural networks for sentence classification (2014), Y. Kim [pdf]
• The stanford coreNLP natural language processing toolkit (2014), C. Manning et al. [pdf] ✨
• Recursive deep models for semantic compositionality over a sentiment treebank (2013), R. Socher 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]

• Automatic speech recognition - A deep learning approach (Book, 2015), D. Yu and L. Deng (Microsoft) [html]
• Speech recognition with deep recurrent neural networks (2013), A. Graves (Hinton) [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. (Hinton) [pdf]

• Mastering the game of Go with deep neural networks and tree search (2016), D. Silver et al. (DeepMind) [pdf] ✨
• Human-level control through deep reinforcement learning (2015), V. Mnih et al. (DeepMind) [pdf] ✨
• Deep learning for detecting robotic grasps (2015), I. Lenz et al. [pdf]
• Playing atari with deep reinforcement learning (2013), V. Mnih et al. (DeepMind) [pdf])

• Unsupervised representation learning with deep convolutional generative adversarial networks (2015), A. Radford et al. [pdf]
• CNN features off-the-Shelf: An astounding baseline for recognition (2014), A. Razavian et al. [pdf] ✨
• Generative adversarial nets (2014), I. Goodfellow et al. (Bengio) [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] ✨
• An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al. [pdf]
• Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. (Bengio) [pdf]
• A practical guide to training restricted boltzmann machines (2010), G. Hinton [pdf]

• TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016), M. Abadi et al. (Google) [pdf]
• Theano: A Python framework for fast computation of mathematical expressions, R. Al-Rfou et al. (Bengio)
• MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc [pdf]
• Caffe: Convolutional architecture for fast feature embedding (2014), Y. Jia et al. [pdf] ✨

Newly released papers which do not meet the criteria but worth reading

• WaveNet: A Generative Model for Raw Audio (2016), A. Oord et al. (DeepMind) [pdf] [web]
• Layer Normalization (2016), J. Ba et al. (Hinton) [pdf]
• Deep neural network architectures for deep reinforcement learning, Z. Wang et al. (DeepMind) [pdf]
• Learning to learn by gradient descent by gradient descent (2016), M. Andrychowicz et al. (DeepMind) [pdf]
• Identity Mappings in Deep Residual Networks (2016), K. He et al. (Microsoft) [pdf]
• Adversarially learned inference (2016), V. Dumoulin et al. [web][pdf]
• Understanding convolutional neural networks (2016), J. Koushik [pdf]
• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size (2016), F. Iandola et al. [pdf]
• Learning to compose neural networks for question answering (2016), J. Andreas et al. [pdf]
• Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016) (Google), S. Levine et al. [pdf]
• Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al. [pdf]
• Eie: Efficient inference engine on compressed deep neural network (2016), S. Han et al. [pdf]
• Adaptive Computation Time for Recurrent Neural Networks (2016), A. Graves [pdf]
• Pixel recurrent neural networks (2016), A. van den Oord et al. (DeepMind) [pdf]

Classic papers (1997~2009) which cause the advent of deep learning era

• 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]

Distinguished deep learning researchers who have published +3 (:sparkles: +6) papers which are on the awesome list (The papers in Hardware / Software, Papers Worth Reading, Classic Papers sections are excluded in counting.)

• Geoffrey Hinton, Google, University of Toronto ✨
• Quoc Le, Google ✨
• Yann LeCun, Facebook, New York University ✨
• Yoshua Bengio, University of Montreal ✨
• Aaron Courville, University of Montreal
• Alex Graves, Google DeepMind
• Andrej Karpathy, OpenAI
• Andrew Ng, Baidu
• Andrew Zisserman, University of Oxford
• Christopher Manning, Stanford University
• David Silver, Google DeepMind
• Dong Yu, Microsoft Research
• Ross Girshick, Facebook
• Kaiming He, Facebook
• Karen Simonyan, Google DeepMind
• Kyunghyun Cho, New York University
• Honglak Lee, University of Michigan
• Ian Goodfellow, OpenAI
• Ilya Sutskever, OpenAI
• Jeff Dean, Google,
• Jeff Donahue, U.C. Berkeley
• Jian Sun, Microsoft Research
• Juergen Schmidhuber, Swiss AI Lab IDSIA
• Li Fei-Fei, Stanford University
• Oriol Vinyals, Google DeepMind
• Pascal Vincent, University of Montreal
• Rob Fergus, Facebook, New York University
• Ruslan Salakhutdinov, CMU
• Tomas Mikolov, Facebook
• Trevor Darrell, U.C. Berkeley

Thank you for all your contributions. Please make sure to read the contributing guide before you make a pull request.

You can follow my facebook page or google plus to get useful information about machine learning and robotics. If you want to have a talk with me, please send me a message to my facebook page.

You can also check out my blog where I share my thoughts on my research area (deep learning for human/robot motions). I got some thoughts while making this list and summerized them in a blog post, "Some trends of recent deep learning researches".

To the extent possible under law, Terry T. Um has waived all copyright and related or neighboring rights to this work.