├── Geoffrey_Hinton ├── papers │ ├── A Fast Learning Algorithm for Deep Belief Nets.pdf │ ├── A Practical Guide to Training Restricted Boltzmann Machines.pdf │ ├── Deep Boltzmann Machine.pdf │ ├── Deep Learning.pdf │ ├── Deep neural networks for acoustic modeling in speech recognition.pdf │ ├── Distilling the Knowledge in a Neural Network.pdf │ ├── Dropout- A Simple Way to Prevent Neural Networks from Overfitting.pdf │ ├── Dynamic Routing Between Capsules.pdf │ ├── Grammar as a Foreign Language.pdf │ ├── ImageNet Classification with Deep Convolutional Neural Networks.pdf │ ├── Improving neural networks by preventing co-adaptation of feature detectors.pdf │ ├── Learning Multiple Layers of Features from Tiny Images.pdf │ ├── Learning representations by back-propagating errors.pdf │ ├── On the importance of initialization and momentum in deep learning.pdf │ ├── Phoneme recognition using time-delay neural networks.pdf │ ├── Rectified Linear Units Improve Restricted Boltzmann Machines.pdf │ ├── Reducing the Dimensionality of Data with Neural Networks.pdf │ ├── Visualizing Data using t-SNE.pdf │ └── deep learning nature.pdf └── readme.txt ├── README.md ├── Yann_LeCun ├── papers │ ├── A Theoretical Analysis of Feature Pooling in Visual Recognition.pdf │ ├── Backpropagation applied to handwritten zip code recognition.pdf │ ├── Character-level convolutional networks for text classification.pdf │ ├── Computing the Stereo Matching Cost with a Convolutional Neural Networ.pdf │ ├── ENERGY-BASED GENERATIVE ADVERSARIAL NETWORKS.pdf │ ├── Gradient-based learning applied to document recognition.pdf │ ├── Learning Hierarchical Features for Scene Labeling.pdf │ ├── OverFeat- Integrated Recognition, Localization and Detection using Convolutional Networks.pdf │ ├── Regularization of neural networks using dropconnect.pdf │ ├── Signature verification using a" siamese" time delay neural network.pdf │ ├── Very Deep Convolutional Networks for Natural Language Processing.pdf │ ├── deep learning nature.pdf │ └── optimal brain damage.pdf └── readme.txt └── Yoshua_Bingio ├── .DS_Store ├── papers ├── A Neural Probabilistic Language Model.pdf ├── BinaryConnect-.pdf ├── CHAR2WAV- END-TO-END SPEECH SYNTHESIS.pdf ├── Convolutional networks for images, speech, and time series.pdf ├── Deep Sparse Rectifier Neural Networks.pdf ├── Greedy Layer-Wise Training of Deep Networks.pdf ├── Learning deep architectures for AI.pdf ├── Learning long-term dependencies with gradient descent is difficult.pdf ├── Maxout Networks.pdf ├── NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE.pdf ├── On the difficulty of training recurrent neural networks.pdf ├── Representation Learning- A Review and New Perspectives.pdf ├── Show, Attend and Tell- Neural Image Caption Generation with Visual Attention.pdf ├── Speech Model Pre-training for End-to-End Spoken Language Understanding.pdf ├── Theano- new features and speed improvements.pdf ├── Understanding the difficulty of training deep feedforward neural networks.pdf ├── deep learning nature.pdf └── generative-adversarial-nets.pdf └── readme.txt /Geoffrey_Hinton/papers/A Fast Learning Algorithm for Deep Belief Nets.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/longpeng2008/Awesome_DNN_Researchers/HEAD/Geoffrey_Hinton/papers/A Fast Learning Algorithm for Deep Belief Nets.pdf -------------------------------------------------------------------------------- /Geoffrey_Hinton/papers/A Practical Guide to Training Restricted Boltzmann Machines.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/longpeng2008/Awesome_DNN_Researchers/HEAD/Geoffrey_Hinton/papers/A Practical Guide to Training Restricted Boltzmann Machines.pdf -------------------------------------------------------------------------------- /Geoffrey_Hinton/papers/Deep Boltzmann Machine.pdf: 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