你需要深度阅读的七十九篇深度学习经典paper

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awesome-free-deep-learning-papers

Survey Review

• Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton ✨

• Deep learning in neural networks: An overview (2015), J. Schmidhuber ✨

• Representation learning: A review and new perspectives (2013), Y. Bengio et al. ✨

Theory Future

• Distilling the knowledge in a neural network (2015), G. Hinton et al.

• Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (2015), A. Nguyen et al.

• How transferable are features in deep neural networks? (2014), J. Yosinski et al. (Bengio)

• Why does unsupervised pre-training help deep learning (2010), E. Erhan et al. (Bengio)

• Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio

Optimization Regularization

• Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al.

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015), S. Loffe and C. Szegedy ✨

• Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), K. He et al. ✨

• Dropout: A simple way to prevent neural networks from overfitting (2014), N. Srivastava et al. (Hinton) ✨

• Adam: A method for stochastic optimization (2014), D. Kingma and J. Ba

• Regularization of neural networks using dropconnect (2013), L. Wan et al. (LeCun)

• Improving neural networks by preventing co-adaptation of feature detectors (2012), G. Hinton et al. ✨

• Spatial pyramid pooling in deep convolutional networks for visual recognition (2014), K. He et al.

• Random search for hyper-parameter optimization (2012) J. Bergstra and Y. Bengio

NetworkModels

• Deep residual learning for image recognition (2016), K. He et al. (Microsoft) ✨

• Going deeper with convolutions (2015), C. Szegedy et al. (Google) ✨

• Fast R-CNN (2015), R. Girshick ✨

• Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman ✨

• Fully convolutional networks for semantic segmentation (2015), J. Long et al. ✨

• OverFeat: Integrated recognition, localization and detection using convolutional networks (2014), P. Sermanet et al. (LeCun)

• Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus ✨

• Maxout networks (2013), I. Goodfellow et al. (Bengio)

• ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. (Hinton) ✨

• Large scale distributed deep networks (2012), J. Dean et al. ✨

• Deep sparse rectifier neural networks (2011), X. Glorot et al. (Bengio)

Image

• Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al. ✨

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al.

• DRAW: A recurrent neural network for image generation (2015), K. Gregor et al.

• Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al.

• Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al.

• DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014), Y. Taigman et al. (Facebook)

• Decaf: A deep convolutional activation feature for generic visual recognition (2013), J. Donahue et al.

• Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al. (LeCun)

• Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis (2011), Q. Le et al.

• Learning mid-level features for recognition (2010), Y. Boureau (LeCun)

Caption

• Show, attend and tell: Neural image caption generation with visual attention (2015), K. Xu et al. (Bengio) ✨

• Show and tell: A neural image caption generator (2015), O. Vinyals et al. ✨

• Long-term recurrent convolutional networks for visual recognition and description (2015), J. Donahue et al. ✨

• Deep visual-semantic alignments for generating image descriptions (2015), A. Karpathy and L. Fei-Fei ✨

Video HumanActivity

• Large-scale video classification with convolutional neural networks (2014), A. Karpathy et al. (FeiFei)

• A survey on human activity recognition using wearable sensors (2013), O. Lara and M. Labrador

• 3D convolutional neural networks for human action recognition (2013), S. Ji et al.

• Deeppose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy

• Action recognition with improved trajectories (2013), H. Wang and C. Schmid

WordEmbedding

• Glove: Global vectors for word representation (2014), J. Pennington et al. ✨

• Sequence to sequence learning with neural networks (2014), I. Sutskever et al.

• Distributed representations of sentences and documents (2014), Q. Le and T. Mikolov (Google) ✨

• Distributed representations of words and phrases and their compositionality (2013), T. Mikolov et al. (Google) ✨

• Efficient estimation of word representations in vector space (2013), T. Mikolov et al. (Google) ✨

• Word representations: a simple and general method for semi-supervised learning (2010), J. Turian (Bengio)

MachineTranslation QnA

• Towards ai-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al.

• Neural machine translation by jointly learning to align and translate (2014), D. Bahdanau et al. (Bengio) ✨

• Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014), K. Cho et al. (Bengio)

• A convolutional neural network for modelling sentences (2014), N. kalchbrenner et al.

• Convolutional neural networks for sentence classification (2014), Y. Kim

• The stanford coreNLP natural language processing toolkit (2014), C. Manning et al.

• Recursive deep models for semantic compositionality over a sentiment treebank (2013), R. Socher et al. ✨

• Natural language processing (almost) from scratch (2011), R. Collobert et al.

• Recurrent neural network based language model (2010), T. Mikolov et al.

Speech Etc.

• Speech recognition with deep recurrent neural networks (2013), A. Graves (Hinton)

• Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups (2012), G. Hinton et al. ✨

• Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition (2012) G. Dahl et al. ✨

RL Robotics

• Mastering the game of Go with deep neural networks and tree search, D. Silver et al. (DeepMind)

• Human-level control through deep reinforcement learning (2015), V. Mnih et al. (DeepMind) ✨

• Deep learning for detecting robotic grasps (2015), I. Lenz et al.

• Playing atari with deep reinforcement learning (2013), V. Mnih et al. (DeepMind) )

Unsupervised

• Building high-level features using large scale unsupervised learning (2013), Q. Le et al. ✨

• Contractive auto-encoders: Explicit invariance during feature extraction (2011), S. Rifai et al. (Bengio)

• An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al.

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

• A practical guide to training restricted boltzmann machines (2010), G. Hinton

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

Hardware Software

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016), M. Abadi et al. (Google)

• MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc

• Caffe: Convolutional architecture for fast feature embedding (2014), Y. Jia et al. ✨

• Theano: new features and speed improvements (2012), F. Bastien et al. (Bengio)

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