ujjwalkarn/deeplearning-papernotes

2016-04

• Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves [arXiv]
• Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models [arXiv]
• Building Machines That Learn and Think Like People [arXiv]
• A Semisupervised Approach for Language Identification based on Ladder Networks [arXiv]
• Deep Networks with Stochastic Depth [arXiv]
• PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents [arXiv]

2016-03

• Attend, Infer, Repeat: Fast Scene Understanding with Generative Models [arXiv]
• Recurrent Batch Normalization [arXiv]
• Neural Language Correction with Character-Based Attention [arXiv]
• Incorporating Copying Mechanism in Sequence-to-Sequence Learning [arXiv]
• How NOT To Evaluate Your Dialogue System [arXiv]
• Adaptive Computation Time for Recurrent Neural Networks [arXiv]
• A guide to convolution arithmetic for deep learning [arXiv]
• Colorful Image Colorization [arXiv]
• Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles [arXiv]
• Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus [arXiv]
• A Persona-Based Neural Conversation Model [arXiv]
• A Character-level Decoder without Explicit Segmentation for Neural Machine Translation [arXiv]
• Multi-Task Cross-Lingual Sequence Tagging from Scratch [arXiv]
• Neural Variational Inference for Text Processing [arXiv]
• Recurrent Dropout without Memory Loss [arXiv]
• One-Shot Generalization in Deep Generative Models [arXiv]
• Recursive Recurrent Nets with Attention Modeling for OCR in the Wild [[arXiv](Recursive Recurrent Nets with Attention Modeling for OCR in the Wild)]
• A New Method to Visualize Deep Neural Networks [[arXiv](A New Method to Visualize Deep Neural Networks)]
• Neural Architectures for Named Entity Recognition [arXiv]
• End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF [arXiv]
• Character-based Neural Machine Translation [arXiv]
• Learning Word Segmentation Representations to Improve Named Entity Recognition for Chinese Social Media [arXiv]

2016-02

• Architectural Complexity Measures of Recurrent Neural Networks [arXiv]
• Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks [arXiv]
• Recurrent Neural Network Grammars [arXiv]
• Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations [arXiv]
• Contextual LSTM (CLSTM) models for Large scale NLP tasks [arXiv]
• Sequence-to-Sequence RNNs for Text Summarization [arXiv]
• Extraction of Salient Sentences from Labelled Documents [arXiv]
• Learning Distributed Representations of Sentences from Unlabelled Data [arXiv]
• Benefits of depth in neural networks [arXiv]
• Associative Long Short-Term Memory [arXiv]
• Generating images with recurrent adversarial networks [arXiv]
• Exploring the Limits of Language Modeling [arXiv]
• Swivel: Improving Embeddings by Noticing What’s Missing [arXiv]
• WebNav: A New Large-Scale Task for Natural Language based Sequential Decision Making [arXiv]
• Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers [arXiv]
• BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 [arXiv]
• Learning Discriminative Features via Label Consistent Neural Network [arXiv]

2016-01

• Pixel Recurrent Neural Networks [arXiv]
• Bitwise Neural Networks [arXiv]
• Long Short-Term Memory-Networks for Machine Reading [arXiv]
• Coverage-based Neural Machine Translation [arXiv]
• Understanding Deep Convolutional Networks [arXiv]
• Training Recurrent Neural Networks by Diffusion [arXiv]
• Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures [arXiv]
• Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism [arXiv]
• Recurrent Memory Network for Language Modeling [arXiv]
• Language to Logical Form with Neural Attention [arXiv]
• Learning to Compose Neural Networks for Question Answering [arXiv]
• The Inevitability of Probability: Probabilistic Inference in Generic Neural Networks Trained with Non-Probabilistic Feedback [arXiv]
• COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images [arXiv]
• Survey on the attention based RNN model and its applications in computer vision [arXiv]

2015-12

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]
• A Diversity-Promoting Objective Function for Neural Conversation Models [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

• Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models [arXiv]
• 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]

2011

• Natural Language Processing (almost) from Scratch [arXiv]