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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

2016-01

2015-12

NLP

Vision

2015-11

NLP

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

2015-09

2015-08

2015-07

2015-06

2015-05

2015-04

  • Correlational Neural Networks [arXiv]

2015-03

2015-02

2015-01

2014-12

2014-11

2014-10

2014-09

2014-08

  • Convolutional Neural Networks for Sentence Classification [arxiv]

2014-07

2014-06

2014-05

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]

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Summaries and notes on Deep Learning research papers

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