一份关于深度学习论文的总结笔记

原文地址：http://www.tuicool.com/articles/miQZBzn

NLP

• Strategies for Training Large Vocabulary Neural Language Models[arXiv]
• Multilingual Language Processing From Bytes[arXiv]
• Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews[ arXiv]
• Target-Dependent Sentiment Classification with Long Short Term Memory[arXiv]
• Reading Text in the Wild with Convolutional Neural Networks [ arXiv]

Vision

• Deep Residual Learning for Image Recognition[arXiv]
• Rethinking the Inception Architecture for Computer Vision [ arXiv]
• Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks [arXiv]
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin [ arXiv]

2015-11

NLP

• Teaching Machines to Read and Comprehend[arxiv]
• Semi-supervised Sequence Learning[arXiv]
• Multi-task Sequence to Sequence Learning[arXiv]
• Alternative structures for character-level RNNs[arXiv]
• Larger-Context Language Modeling[arXiv]
• A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding[arXiv]
• Towards Universal Paraphrastic Sentence Embeddings [ arXiv]
• BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies [arXiv]
• Sequence Level Training with Recurrent Neural Networks [ arXiv]
• Natural Language Understanding with Distributed Representation [ arXiv]
• sense2vec – A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings [arXiv]
• LSTM-based Deep Learning Models for non-factoid answer selection [ arXiv]

Programs

• Neural Random-Access Machines [ arxiv]
• Neural Programmer: Inducing Latent Programs with Gradient Descent [ arXiv]
• Neural Programmer-Interpreters [ arXiv]
• Learning Simple Algorithms from Examples [ arXiv]
• Neural GPUs Learn Algorithms [ arXiv]
• On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models [arXiv]

Vision

• ReSeg: A Recurrent Neural Network for Object Segmentation [ arXiv]
• Deconstructing the Ladder Network Architecture [ arXiv]
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [arXiv]

General

• Towards Principled Unsupervised Learning [ arXiv]
• Dynamic Capacity Networks [ arXiv]
• Generating Sentences from a Continuous Space [ arXiv]
• Net2Net: Accelerating Learning via Knowledge Transfer [ arXiv]
• A Roadmap towards Machine Intelligence [ arXiv]
• Session-based Recommendations with Recurrent Neural Networks [ arXiv]
• Regularizing RNNs by Stabilizing Activations [ arXiv]

2015-10

• A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification[ arXiv]
• Attention with Intention for a Neural Network Conversation Model[arXiv]
• Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network [arXiv]
• A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas [arXiv]
• A Primer on Neural Network Models for Natural Language Processing [ arXiv]

2015-09

• Character-level Convolutional Networks for Text Classification[arXiv]
• A Neural Attention Model for Abstractive Sentence Summarization[arXiv]
• Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games [arXiv]

2015-08

• Listen, Attend and Spell [ arxiv]
• Character-Aware Neural Language Models[arXiv]
• Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs [arXiv]
• Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation [arXiv]

2015-07

• Semi-Supervised Learning with Ladder Networks [ arXiv]
• Document Embedding with Paragraph Vectors[arXiv]
• Training Very Deep Networks[arXiv]

2015-06

• A Neural Network Approach to Context-Sensitive Generation of Conversational Responses [arXiv]
• Document Embedding with Paragraph Vectors[arXiv]
• A Neural Conversational Model[arXiv]
• Skip-Thought Vectors[arXiv]
• Pointer Networks[arXiv]
• Spatial Transformer Networks[arXiv]
• Tree-structured composition in neural networks without tree-structured architectures [arXiv]
• Visualizing and Understanding Neural Models in NLP [ arXiv]
• Learning to Transduce with Unbounded Memory [ arXiv]
• Ask Me Anything: Dynamic Memory Networks for Natural Language Processing [ arXiv]
• Deep Knowledge Tracing[arXiv]

2015-05

• ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks[arXiv]
• Reinforcement Learning Neural Turing Machines [ arXiv]

2015-04

• Correlational Neural Networks [ arXiv]

2015-03

• Distilling the Knowledge in a Neural Network[arXiv]
• End-To-End Memory Networks[arXiv]
• Neural Responding Machine for Short-Text Conversation [ arXiv]
  • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift[arXiv]

2015-02

• Text Understanding from Scratch[arXiv]
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention[arXiv]

2015-01

2014-12

• Learning Longer Memory in Recurrent Neural Networks [ arXiv]
• Neural Turing Machines[arxiv]
• Grammar as a Foreign Langauage[arXiv]
• On Using Very Large Target Vocabulary for Neural Machine Translation[arXiv]
• Effective Use of Word Order for Text Categorization with Convolutional Neural Networks [arXiv]
• Multiple Object Recognition with Visual Attention [ arXiv]

2014-11

2014-10

• Learning to Execute[arXiv]

2014-09

• Sequence to Sequence Learning with Neural Networks[arXiv]
• Neural Machine Translation by Jointly Learning to Align and Translate[arxiv]
• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches[arXiv]
• Recurrent Neural Network Regularization[arXiv]
• Very Deep Convolutional Networks for Large-Scale Image Recognition [ arXiv]
• Going Deeper with Convolutions [ arXiv]

2014-08

• Convolutional Neural Networks for Sentence Classification [ arxiv]

2014-07

2014-06

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation[ arXiv]
• Recurrent Models of Visual Attention[arXiv]
• Generative Adversarial Networks [ arXiv]

2014-05

• Distributed Representations of Sentences and Documents[arXiv]

2014-04

• A Convolutional Neural Network for Modelling Sentences [ arXiv]

2014-03

2014-02

2014-01

2013

• Visualizing and Understanding Convolutional Networks [ arXiv]
• DeViSE: A Deep Visual-Semantic Embedding Model [ pub]
• Maxout Networks [ arXiv]
• Exploiting Similarities among Languages for Machine Translation [ arXiv]
• Efficient Estimation of Word Representations in Vector Space [ arXiv]