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 : Based on discussions
• 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!

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

(Total 82 papers)

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]
• Why does unsupervised pre-training help deep learning (2010), E. Erhan et al. (Bengio) [pdf]
• Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio [pdf]

Optimization / Regularization

• Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari 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. (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]
• 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] ✨
• Going deeper with convolutions (2015), C. Szegedy et al. (Google) [pdf] ✨
• Fast R-CNN (2015), R. Girshick [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]
• 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

• 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 [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.

• 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, D. Silver et al. (DeepMind) [[pdf]](Mastering the game of Go with deep neural networks and tree search)
• 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] ✨
• 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]
• 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] ✨
• Theano: new features and speed improvements (2012), F. Bastien et al. (Bengio) [pdf]

License

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