robertsdionne/neural-network-papers · GitHub

https://github.com/robertsdionne/neural-network-papers

neural-network-papers

Table of Contents
    Other Lists
    Surveys
    Books
    Datasets
    Pretrained Models
    Programming Frameworks
    Learning to Compute
    Natural Language Processing
    Convolutional Neural Networks
    Recurrent Neural Networks
    Convolutional Recurrent Neural Networks
    Adversarial Neural Networks
    Autoencoders
    Restricted Boltzmann Machines
    Biologically Plausible Learning
    Supervised Learning
    Unsupervised Learning
    Reinforcement Learning
    Theory
    Quantum Computing
    Training Innovations
    Parallel Training
    Weight Compression
    Numerical Precision
    Numerical Optimization
    Motion Planning
    Simulation
    Hardware
    Cognitive Architectures
    Computational Creativity
    Cryptography
    Distributed Computing
    Clustering

Neural network paper list | Eniod's Blog

https://haduonght.wordpress.com/2015/12/23/neural-network-paper-list/

Based on https://github.com/robertsdionne/neural-network-papers

Computational Network Toolkit (CNTK) - Home

https://cntk.codeplex.com/

CNTK, the Computational Network Toolkit by Microsoft Research, is a unified deep-learning toolkit

CNTK allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

Microsoft Research による、Windows と Linux に対応した deep learning のツールキット。
下記ページに速度性能を比較した記事がある。

Microsoft Computational Network Toolkit offers most efficient distributed deep learning computational performance
http://blogs.technet.com/b/inside_microsoft_research/archive/2015/12/07/microsoft-computational-network-toolkit-offers-most-efficient-distributed-deep-learning-computational-performance.aspx

[1506.03340] Teaching Machines to Read and Comprehend

http://arxiv.org/abs/1506.03340

In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data.

The Impatient Reader
The Attentive Reader is able to focus on the passages of a context document that are most likely to inform the answer to the query. We can go further by equipping the model with the ability to reread from the document as each query token is read.

文書を学習して、穴埋め問題を解く。Impatient Reader と Attentive Reader の比較。

スライドはこの記事を参照。