Awesome - Most Cited Deep Learning Papers

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.

Awesome list criteria

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

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Table of Contents

• 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.

Survey / Review

• Deep learning (Book, 2016), Goodfellow et al. (Bengio) [html]
• Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton [html]
• Deep learning in neural networks: An overview (2015), J. Schmidhuber [pdf]
• Representation learning: A review and new perspectives (2013), Y. Bengio et al. [pdf]

Theory / Future

• 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]

Optimization / Regularization

• 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]

Network Models

• 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]

Image

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

Caption

• 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]

Video / Human Activity

• 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]

Word Embedding

• 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]

Machine Translation / QnA

• 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]

Speech / Etc.

• 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]

RL / Robotics

• 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

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

Hardware / Software

• 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]

Papers Worth Reading

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

• 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]
• LSTM: A search space odyssey (2015), K. Greff et al. [pdf]
• Training very deep networks (2015), R. Srivastava et al. [pdf]

Classic Papers

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 Researchers

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

• Jian Sun, Microsoft Research
• 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, Microsoft Research
• 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
• 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

Acknowledgement

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".

License

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

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