Skip to content

An open source implementation of DeepMind : Teaching Machines to Read and Comprehend

License

Notifications You must be signed in to change notification settings

ejls/Deep-Question-Answering

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Teaching Machines to Read and Comprehend

This repository contains an implementation of the models Teaching Machines to Read and Comprehend proposed by Karl Moritz Hermann and al., NIPS, 2015.

Models are implemented using Theano and Blocks. Datasets are implemented using Fuel.

Reference

Teaching Machines to Read and Comprehend, by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman and Phil Blunsom, Neural Information Processing Systems, 2015.

Credits

Alex Auvolat

Thomas Mesnard

Étienne Simon

About

An open source implementation of DeepMind : Teaching Machines to Read and Comprehend

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages