This chapter is associated with the papers published in deep learning.
Imagenet classification with deep convolutional neural networks : [Paper]
Convolutional Neural Networks for Sentence Classification : [Paper]
Large-scale Video Classification with Convolutional Neural Networks : [Paper]
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks : [Paper]
Deep convolutional neural networks for LVCSR : [Paper]
Face recognition: a convolutional neural-network approach : [Paper]
An empirical exploration of recurrent network architectures : [Paper]
LSTM: A search space odyssey : [Paper]
On the difficulty of training recurrent neural networks : [Paper]
Learning to forget: Continual prediction with LSTM : [Paper]
Extracting and composing robust features with denoising autoencoders : [Paper]
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion : [Paper]
Adversarial Autoencoders : [Paper]
Autoencoders, Unsupervised Learning, and Deep Architectures : [Paper]
Reducing the Dimensionality of Data with Neural Networks : [Paper]
Exploiting generative models discriminative classifiers : [Paper]
Semi-supervised Learning with Deep Generative Models : [Paper]
Generative Adversarial Nets : [Paper]
Generalized Denoising Auto-Encoders as Generative Models : [Paper]
Stochastic Backpropagation and Approximate Inference in Deep Generative Models : [Paper]
Probabilistic models of cognition: exploring representations and inductive biases : [Paper]
On deep generative models with applications to recognition : [Paper]
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift : [Paper]
Dropout: A Simple Way to Prevent Neural Networks from Overfitting : [Paper]
Training Very Deep Networks : [Paper]
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification : [Paper]
Large Scale Distributed Deep Networks : [Paper]
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks : [Paper]
Representation Learning: A Review and New Perspectives : [Paper]
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets : [Paper]
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks : [Paper]
Distilling the Knowledge in a Neural Network : [Paper]
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition : [Paper]
How transferable are features in deep neural networks? : [Paper]
Human-level control through deep reinforcement learning : [Paper]
Playing Atari with Deep Reinforcement Learning : [Paper]
Continuous control with deep reinforcement learning : [Paper]
Deep Reinforcement Learning with Double Q-Learning : [Paper]
Dueling Network Architectures for Deep Reinforcement Learning : [Paper]
Deep Residual Learning for Image Recognition : [Paper]
Very Deep Convolutional Networks for Large-Scale Image Recognition : [Paper]
Multi-column Deep Neural Networks for Image Classification : [Paper]
DeepID3: Face Recognition with Very Deep Neural Networks : [Paper]
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps : [Paper]
Deep Image: Scaling up Image Recognition : [Paper]
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description : [Paper]
ImageNet Classification with Deep Convolutional Neural Networks : [Paper]
Learning Deep Features for Scene Recognition using Places Database : [Paper]
Scalable Object Detection using Deep Neural Networks : [Paper]
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks : [Paper]
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks : [Paper]
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition : [Paper]
What is the best multi-stage architecture for object recognition? : [Paper]
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description : [Paper]
Learning Spatiotemporal Features With 3D Convolutional Networks : [Paper]
Describing Videos by Exploiting Temporal Structure : [Paper]
Convolutional Two-Stream Network Fusion for Video Action Recognition : [Paper]
Temporal segment networks: Towards good practices for deep action recognition : [Paper]
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention : [Paper]
Mind's Eye: A Recurrent Visual Representation for Image Caption Generation : [Paper]
Generative Adversarial Text to Image Synthesis : [Paper]
Deep Visual-Semantic Al60ignments for Generating Image Descriptions : [Paper]
Show and Tell: A Neural Image Caption Generator : [Paper]
Distributed Representations of Words and Phrases and their Compositionality : [Paper]
Efficient Estimation of Word Representations in Vector Space : [Paper]
Sequence to Sequence Learning with Neural Networks : [Paper]
Neural Machine Translation by Jointly Learning to Align and Translate : [Paper]
Get To The Point: Summarization with Pointer-Generator Networks : [Paper]
Attention Is All You Need : [Paper]
Convolutional Neural Networks for Sentence Classification : [Paper]
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups : [Paper]
Towards End-to-End Speech Recognition with Recurrent Neural Networks : [Paper]
Speech recognition with deep recurrent neural networks : [Paper]
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition : [Paper]
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin : [Paper]
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin : [Paper]
A novel scheme for speaker recognition using a phonetically-aware deep neural network : [Paper]