Papers

This chapter is associated with the papers published in deep learning.

Models

Convolutional Networks

• 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]

  

Recurrent Networks

• 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]

  

Autoencoders

• 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]

  

Generative Models

• 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]

  

Probabilistic Models

• 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]

  

Core

Optimization

• 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]

  

Representation Learning

• 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]

  

Understanding and Transfer Learning

• 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]

  

Reinforcement Learning

• 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]

  

Applications

Image Recognition

• 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]

  

Object Recognition

• 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]

  

Action Recognition

• 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]

  

Caption Generation

• 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]

  

Natural Language Processing

• 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]

  

Speech Technology

• 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]