kod3r/awesome-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 : Based on discussions
• 2015 : +100 citations (✨ +200)
• 2014 : +200 citations (✨ +400)
• 2013 : +300 citations (✨ +600)
• 2012 : +400 citations (✨ +800)
• 2011 : +500 citations (✨ +1000)
• 2010 : +600 citations (✨ +1200)

This criteria is not a strict baseline, but a flexible guideline for being added to the awesome list. (Since the number of citations is affected by the research area, some papers under the threshold may be added to the list while some over the threshold may not.)

I need your contributions! Please add important papers to the awesome list as well as delete some papers if they are considered as out-of-date. I may have made mistake in the list (e.g. typo, missing ✨), so please correct if you find any errors. Thank you!

Table of Contents

• Survey / Review
• Theory / Future
• Optimization / Regularization
• NetworkModels
• Image
• Caption
• Video / HumanActivity
• WordEmbedding
• MachineTranslation / QnA
• Speech / Etc.
• RL / Robotics
• Unsupervised
• Hardware / Software

Survey / Review

• 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] ✨

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]
• 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] ✨
• Spatial pyramid pooling in deep convolutional networks for visual recognition (2014), K. He et al. [pdf]
• Random search for hyper-parameter optimization (2012) J. Bergstra and Y. Bengio [pdf]

NetworkModels

• 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] ✨
• Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman [pdf] ✨
• Fully convolutional networks for semantic segmentation (2015), J. Long et al. [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 hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis (2011), Q. Le et al. [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 / HumanActivity

• Large-scale video classification with convolutional neural networks (2014), A. Karpathy et al. (FeiFei) [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]
• Deeppose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy [pdf]
• Action recognition with improved trajectories (2013), H. Wang and C. Schmid [pdf]

WordEmbedding

• Glove: Global vectors for word representation (2014), J. Pennington et al. [pdf] ✨
• Sequence to sequence learning with neural networks (2014), I. Sutskever 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]

MachineTranslation / 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] ✨
• 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] ✨

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

• 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]

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