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Most Cited Deep Learning Papers

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 ( :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)

I need your contributions! Please read thecontributing guide before you make a pull request.

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
  • Distinguished Researchers

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

Survey / Review

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

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] :sparkles:
  • 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] :sparkles:
  • Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), K. He et al. (Microsoft) [pdf] :sparkles:
  • Dropout: A simple way to prevent neural networks from overfitting (2014), N. Srivastava et al. (Hinton) [pdf] :sparkles:
  • 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] :sparkles:
  • 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] :sparkles:
  • 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] :sparkles:
  • Fast R-CNN (2015), R. Girshick (Microsoft) [pdf] :sparkles:
  • Fully convolutional networks for semantic segmentation (2015), J. Long et al. [pdf] :sparkles:
  • Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman [pdf] :sparkles:
  • 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] :sparkles:
  • 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] :sparkles:
  • Large scale distributed deep networks (2012), J. Dean et al. [pdf] :sparkles:
  • 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] :sparkles:
  • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. [pdf] :sparkles:
  • 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] :sparkles:
  • 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] :sparkles:
  • Decaf: A deep convolutional activation feature for generic visual recognition (2013), J. Donahue et al. [pdf] :sparkles:
  • 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] :sparkles:
  • Show and tell: A neural image caption generator (2015), O. Vinyals et al. [pdf] :sparkles:
  • Long-term recurrent convolutional networks for visual recognition and description (2015), J. Donahue et al. [pdf] :sparkles:
  • Deep visual-semantic alignments for generating image descriptions (2015), A. Karpathy and L. Fei-Fei [pdf] :sparkles:

Video / Human Activity

  • Large-scale video classification with convolutional neural networks (2014), A. Karpathy et al. (FeiFei) [pdf] :sparkles:
  • 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] :sparkles:
  • Distributed representations of sentences and documents (2014), Q. Le and T. Mikolov [pdf] (Google) :sparkles:
  • Distributed representations of words and phrases and their compositionality (2013), T. Mikolov et al. (Google) [pdf] :sparkles:
  • Efficient estimation of word representations in vector space (2013), T. Mikolov et al. (Google) [pdf] :sparkles:
  • 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] :sparkles:
  • Sequence to sequence learning with neural networks (2014), I. Sutskever et al. [pdf] :sparkles:
  • 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] :sparkles:
  • Recursive deep models for semantic compositionality over a sentiment treebank (2013), R. Socher et al. [pdf] :sparkles:
  • Natural language processing (almost) from scratch (2011), R. Collobert et al. [pdf] :sparkles:
  • 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] :sparkles:
  • Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition (2012) G. Dahl et al. [pdf] :sparkles:
  • 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] :sparkles:
  • Human-level control through deep reinforcement learning (2015), V. Mnih et al. (DeepMind) [pdf] :sparkles:
  • 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] :sparkles:
  • Contractive auto-encoders: Explicit invariance during feature extraction (2011), S. Rifai et al. (Bengio) [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] :sparkles:

Papers Worth Reading

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

  • 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]
  • Recent Advances in Convolutional Neural Networks (2015), J. Gu et al.( http://arxiv.org/pdf/1512.07108 )
  • LSTM: A search space odyssey (2015), K. Greff et al. [pdf]

Distinguished Researchers

Distinguished deep learning researchers who have published +3 ( :sparkles: +6) papers which are on the awesome list

Acknowledgement

Thank you for all your contributions. Please make sure to read thecontributing 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). Thank you!

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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