(zhuan) Recurrent Neural Network

简介: Recurrent Neural Network 2016年07月01日  Deep learning  Deep learning 字数:24235  this blog from: http://jxgu.

 

Recurrent Neural Network

 2016年07月01日  Deep learning  Deep learning 字数:24235 
 this blog from: http://jxgu.cc/blog/recent-advances-in-RNN.html 

References

Baisc

Improvements

  1. 20170326 Learning Simpler Language Models with the Delta Recurrent Neural Network Framework
  2. 20170316 Machine Learning on Sequential Data Using a Recurrent Weighted Average
  3. 20161029 Phased LSTM Accelerating Recurrent Network Training for Long or Event based Sequences
  4. 20161020 Using Fast Weights to Attend to the Recent Past
  5. 20161017 Interactive Attention for Neural Machine Translation
  6. 20160908 LSTM GRU Highway and a Bit of Attention An Empirical Overview for Language Modeling in Speech Recognition
  7. 20160811 Recurrent Highway Networks
  8. 20160721 Layer Normalization
  9. 20160713 Recurrent Memory Array Structures
  10. 20160524 Sequential Neural Models with Stochastic Layers
  11. 20160513 LSTM with Working Memory
  12. 20160412 Recurrent Batch Normalization
  13. 20160209 Associative Long Short-Term Memory
  14. 20151214 Memory-based control with recurrent neura networks
  15. 20151105 Quasi-Recurrent Neural Networks
  16. 20150503 ReNet A Recurrent Neural Network Based Alternative to Convolutional Networks
  17. 20150331 End-To-End Memory Networks
  18. 20150209 Gated Feedback Recurrent Networks
  19. 20150204 Spatial Transformer Networks
  20. 20140908 Recurrent Neural Network Regularization

Image captioning

  1. 20170330 Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
  2. 20170218 MAT A Multimodal Attentive Translator for Image Captioning
  3. 20161214 Optimization of image description metrics using policy gradient methods
  4. 20161212 Text guided Attention Model for Image Captioning
  5. 20161205 Recurrent Image Captioner Describing Images with Spatial-Invariant Transformation and Attention Filtering
  6. 20161203 Areas of Attention for Image Captioning
  7. 20160809 Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders
  8. 20160706 Sort Story Sorting Jumbled Images and Captions into Stories
  9. 20160701 Domain Adaptation for Neural Networks by Parameter Augmentation
  10. 20160620 Variational Autoencoder for Deep Learning of Images Labels and Captions
  11. 20160615 Image Caption Generation with Text-Conditional Semantic Attention
  12. 20160607 Encode Review and Decode Reviewer Module for Caption Generation
  13. 20160605 Multimodal Residual Learning for Visual QA
  14. 20160531 Attention Correctness in Neural Image Captioning
  15. 20160512 Movie Description
  16. 20160503 Improving Image Captioning by Concept-based Sentence Reranking
  17. 20160501 Delving Deeper into Convolutional Networks for Learning Video Representations
  18. 20160419 Show Attend and Tell Neural Image Caption Generation with Visual Attention
  19. 20160406 Improving LSTM-based Video Descriptionwith Linguistic Knowledge Mined from Text
  20. 20160404 Image Captioning with Deep Bidirectional LSTMs
  21. 20160330 Rich Image Captioning in the Wild
  22. 20160330 Dense Image Representation with Spatial Pyramid VLAD Coding of CNN for Locally Robust Captioning
  23. 20160328 Generating Visual Explanations
  24. 20160328 Attend Infer Repeat:Fast Scene Understanding with Generative Models
  25. 20160324 A Diagram Is Worth A Dozen Images
  26. 20160301 ORDER-EMBEDDINGS OFIMAGES AND LANGUAGE
  27. 20160228 Generating Visual Explanations
  28. 20151117 Deep Compositional Captioning Describing Novel Object Categories without Paired Training Data
  29. 20151111 Deep Multimodal Semantic Embeddings for Speech and Images
  30. 20151109 Generating Images From Captions With Attention
  31. 20151027 Learning Deep Representations of Fine-Grained Visual-Descriptions
  32. 20151026 Video Paragraph Captioning using Hierarchical RecurrentNeuralNetworks
  33. 20151013 Summarization based Video Caption via DeepNeuralNetworks
  34. 20150916 Guiding Long-Short Term Memory for Image Caption Generation
  35. 20150829 Multimodal Convolutional Neural Networks for Matching Image and Sentence
  36. 20150604 The Long Short Story of Movie Description
  37. 20150604 Jointly Modeling Embedding and Translation to Bridge Video and Language
  38. 20150420 Show and Tell A Neural Image Caption Generator
  39. 20150414 Deep Visual-Semantic Alignments for Generating Image Descriptions
  40. 20150303 Sequence to Sequence Video to Text
  41. 20150227 Describing Videos by Exploiting Temporal Structure
  42. 20150212 Phrase based Image Captioning
  43. 20150210 Show Attend and Tell Neural Image Caption Generation with Visual Attention

Image generation

  1. 20170411 A neural representation of sketch drawings
  2. 20161214 VAE vs GAN
  3. 20160819 Pixel Recurrent Neural Networks
  4. 20160726 Semantic Image Inpainting with Perceptual and Contextual Losses
  5. 20160629 Towards Conceptual Compression
  6. 20160619 Generating Images Part by Part with Composite Generative Adversarial Networks
  7. 20160616 Conditional Image Generation with PixelCNN Decoders
  8. 20160610 Improved Techniques for Training GANs
  9. 20160610 Deep Directed Generative Models with Energy-Based Probability Estimation
  10. 20160605 Generative Adversarial Text to Image Synthesis
  11. 20160529 Generating images with recurrent adversarial networks
  12. 20160526 Domain-Adversarial Training of Neural Networks
  13. 20160526 Adversarial Autoencoders
  14. 20160320 Segmentation from Natural Language Expressions
  15. 20160229 Generating Images from Captions with Attention
  16. 20151119 Unsupervised Learning of Visual Structure using Predictive Generative Networks
  17. 20151109 Generating Images From Captions With Attention
  18. 20150216 DRAW A Recurrent Neural Network For Image Generation
  19. 20140610 Generative Adversarial Networks

Visual question answering

  1. 20160926 The Color of the Cat is Gray 1 Million Full Sentences Visual Question Answering
  2. 20160620 DualNet Domain-Invariant Network for Visual Question Answering
  3. 20160612 Training Recurrent Answering Units with Joint Loss Minimization for VQA
  4. 20160605 Multimodal Residual Learning for Visual QA
  5. 20160509 Ask Your Neurons A Deep Learning Approach to Visual Question Answering
  6. 20160504 Leveraging Visual Question Answering for Image-Caption Ranking
  7. 20160420 Question Answering via Integer Programming over Semi-Structured Knowledge
  8. 20160406 A Focused Dynamic Attention Model for VQA
  9. 20160319 Generating Natural Questions About an Image
  10. 20160309 Image Captioning and Visual QuestionAnswering Based on Attributes and TheirRelated External Knowledge
  11. 20160304 Dynamic Memory Networks for Visual and Textual Question Answering
  12. 20160208 Visualizing and Understanding Neural Models in NLP
  13. 20160202 Where To Look Focus Regions for Visual Question Answering
  14. 20151209 MovieQA Understanding Stories in Movies through Question-Answering
  15. 20151123 Where to look Focus regions for visual question answering
  16. 20151118 Learning to Answer Questions From Image Using Convolutional Neural Network
  17. 20151118 Compositional Memory for Visual Question Answering
  18. 20151118 An attention based convolutional neural network for visual question answering
  19. 20151117 Ask, Attend and Answer Exploring question-guided spatial attention for visual question answering
  20. 20151112 LSTM-based Deep Learning Models for Non-factoid Answer Selection
  21. 20151111 Visual7W Grounded Question Answering in Images
  22. 20151109 Explicit Knowledge-based Reasoning for Visual Question Answering
  23. 20151107 Stacked attention networks for image question answering
  24. 20151107 Simple Baseline for Visual Question Answering
  25. 20150521 Are you talking to a machine? dataset and methods for multilingual image question answering
  26. 20150508 Exploring Models and Data for Image Question-Answering
  27. 20150508 Exploring Models and Data for Image Question Answering
  28. 20150505 Ask Your Neurons A Neural-based Approach to Answering Questions about Images
  29. 20150503 VQA Visual Question Answering

Natural language processing

  1. 20170330 Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
  2. 20170324 Sequence-to-Sequence Models Can Directly Transcribe Foreign Speech
  3. 20170302 Controllable Text generation
  4. 20170208 A Hybrid Convolutional Variational Autoencoder for Text Generation
  5. 20161220 Hierarchical Softmax
  6. 20160818 Full Resolution Image Compression with Recurrent Neural Networks
  7. 20160803 Learning Online Alignments with Continuous Rewards Policy Gradient
  8. 20160803 Dependency-based Convolutional Neural Networks
  9. 20160726 An Actor-Critic Algorithm for Sequence Prediction
  10. 20160722 Syntax-based Attention Model for Natural Language Inference
  11. 20160718 Neural Machine Translation with Recurrent Attention Modeling
  12. 20160715 Neural Tree Indexers for Text Understanding
  13. 20160715 Neural Machine Translation with
  14. Recurrent Attention Modeling
  15. 20160715 Attention-over-Attention Neural Networks for Reading Comprehension
  16. 20160710 CHARAGRAM Embedding Words and Sentences via Character n-grams
  17. 20160705 Chains of Reasoning over Entities Relations and Text using Recurrent Neural Networks
  18. 20160703 快进连接改进Attention机制 深度学习提升机器翻译效果
  19. 20160621 Topic Augmented Neural Response Generation with a Joint Attention Mechanism
  20. 20160615 The Enemy in Your Own Camp How Well Can We Detect Statistically-Generated Fake Reviews–An Adversarial Study
  21. 20160613 Attention-based Multimodal Neural Machine Translation
  22. 20160607 Memory-enhanced Decoder for Neural Machine Translation
  23. 20160606 Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification
  24. 20160606 A Decomposable Attention Model for Natural Language Inference
  25. 20160605 Deep Reinforcement Learning for Dialogue Generation
  26. 20160524 Hierarchical Memory Networks
  27. 20160524 Combining Recurrent and Convolutional Neural Networks for Relation Classification
  28. 20160509 Parse tree
  29. 20160428 Crafting Adversarial Input Sequences for Recurrent Neural Networks
  30. 20160407 Sentence Level Recurrent Topic Model Letting Topics Speak for Themselves
  31. 20160406 A Recurrent Latent Variable Model for Sequential Data
  32. 20160404 Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
  33. 20160401 Building Machines That Learn and Think Like People
  34. 20160323 Latent Predictor Networks for Code Generation
  35. 20160322 Fully Convolutional Attention Localization Networks Efficient AttentionLocalization for Fine-Grained Recognition
  36. 20160221 Learning Semantic Representations using Relations
  37. 20160219 Contextual LSTM (CLSTM) models for Large scale NLP tasks
  38. 20160208 Efficient Algorithms for Adversarial Contextual Learning
  39. 20160207 Exploring the Limits of Language Modeling
  40. 20160206 WebNav A New Large-Scale Task for Natural Language based Sequential Decision Making
  41. 20160206 Recurrent Memory Network for Language Modeling
  42. 20160206 Multi-Way Multilingual Neural Machine Translation with a Shared Attention Mechanism
  43. 20160201 Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers
  44. 20160110 Strategies for Training Large Vocabulary Neural Language Models
  45. 20151227 Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews
  46. 20151215 Increasing the Action Gap New Operators for Reinforcement Learning
  47. 20151203 Target-Dependent Sentiment Classification with Long Short Term Memory
  48. 20151203 Neural Enquirer Learning to Query Tables in Natural Language
  49. 20151201 Multilingual Language Processing From Bytes
  50. 20151119 Multi-task Sequence to Sequence Learning
  51. 20151119 Alternative structures for character-level RNNs
  52. 20151111 Larger-Context Language Modeling
  53. 20151104 Semi-supervised Sequence Learning
  54. 20151101 A Unified Tagging Solution Bidirectional LSTM Recurrent Neural Network with Word Embedding
  55. 20151031 Top down Tree LSTM Networks
  56. 20151029 Attention with Intention for a Neural Network Conversation Model
  57. 20151026 Thinking on your Feet Reinforcement Learning for Incremental Language Tasks
  58. 20151013 A Sensitivity Analysis of Convolutional Neural Networks for Sentence Classification
  59. 20151011 A Diversity-Promoting Objective Function for Neural Conversation Models
  60. 20150902 A Neural Attention Model for Abstractive Sentence Summarization
  61. 20150822 Towards Neural Network-based Reasoning
  62. 20150726 Improved Semantic Representations From Tree-Structured Long Short TermMemory Networks
  63. 20150629 Document Embedding with Paragraph Vectors
  64. 20150626 On Using Very Large Target Vocabulary for Neural Machine Translation
  65. 20150622 Skip-Thought Vectors
  66. 20150619 Deep Knowledge Tracing
  67. 20150619 A Neural Conversational Model
  68. 20150617 Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
  69. 20150610 Teaching Machines to Read and Comprehend
  70. 20150609 Scheduled sampling for sequence prediction with recurrent neural networks
  71. 20150531 A Neural Network Approach to Context Sensitive Generation of Conversational Responses
  72. 20150427 Neural Responding Machine for Short-Text Conversation
  73. 20150205 Character-level Convolutional Networks for Text Classification
  74. 20141223 Grammar as a Foreign Langauage
  75. 20141017 Learning to Execute
  76. 20140910 Sequence to Sequence Learning with Neural Networks
  77. 20140903 On the Properties of Neural Machine Translation Encoder-Decoder Approaches
  78. 20140901 Neural Machine Translation by Jointly Learning to Align and Translate
  79. 20140624 Recurrent Models of Visual Attention
  80. 20140603 Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
  81. 20140516 Distributed Representations of Sentences and Documents
  82. 20050909 METEOR An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments
  83. 20010917 Bleu a method for automatic evaluation of machine translation
 
 

 

相关文章
|
6月前
|
机器学习/深度学习 自然语言处理 算法
[BPE]论文实现:Neural Machine Translation of Rare Words with Subword Units
[BPE]论文实现:Neural Machine Translation of Rare Words with Subword Units
41 0
|
机器学习/深度学习 数据采集 算法
卷积神经网络(Convolutional Neural Network
机器学习是一种人工智能技术,通过让计算机从数据中学习和提取规律,从而实现对未知数据的预测和决策。卷积神经网络(Convolutional Neural Network,简称 CNN)是机器学习中的一种方法,主要应用于图像识别、语音识别、文本处理等领域。
117 4
《Multi-Task Multi-Network Joint-Learning of Deep Residual Networks and Cycle-Consistency Generative Adversarial Networks for Robust Speech Recognition》电子版地址
Multi-Task Multi-Network Joint-Learning of Deep Residual Networks and Cycle-Consistency Generative Adversarial Networks for Robust Speech Recognition
102 0
《Multi-Task Multi-Network Joint-Learning of Deep Residual Networks and Cycle-Consistency Generative Adversarial Networks for Robust Speech Recognition》电子版地址
|
机器学习/深度学习 数据挖掘
Paper:《Generating Sequences With Recurrent Neural Networks》的翻译和解读
Paper:《Generating Sequences With Recurrent Neural Networks》的翻译和解读
Paper:《Generating Sequences With Recurrent Neural Networks》的翻译和解读
|
机器学习/深度学习 数据建模
2_Recurrent Neural Networks (RNNs)循环神经网络 —Simple RNNs
2_Recurrent Neural Networks (RNNs)循环神经网络 —Simple RNNs
188 0
2_Recurrent Neural Networks (RNNs)循环神经网络 —Simple RNNs
|
机器学习/深度学习 数据挖掘 计算机视觉
CV:翻译并解读2019《A Survey of the Recent Architectures of Deep Convolutional Neural Networks》第四章(一)
CV:翻译并解读2019《A Survey of the Recent Architectures of Deep Convolutional Neural Networks》第四章
CV:翻译并解读2019《A Survey of the Recent Architectures of Deep Convolutional Neural Networks》第四章(一)
|
机器学习/深度学习 传感器 文字识别
Paper:《Generating Sequences With Recurrent Neural Networks》的翻译和解读(三)
Paper:《Generating Sequences With Recurrent Neural Networks》的翻译和解读
|
机器学习/深度学习 存储 文字识别
Paper:《Generating Sequences With Recurrent Neural Networks》的翻译和解读(一)
Paper:《Generating Sequences With Recurrent Neural Networks》的翻译和解读
|
机器学习/深度学习 XML 缓存
Paper:《Generating Sequences With Recurrent Neural Networks》的翻译和解读(二)
Paper:《Generating Sequences With Recurrent Neural Networks》的翻译和解读