/deeplearning-papernotes

Summaries and notes on Deep Learning research papers

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]