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| author | Thomas Mesnard <thomas.mesnard@ens.fr> | 2016-03-01 00:27:15 +0100 |
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| committer | Thomas Mesnard <thomas.mesnard@ens.fr> | 2016-03-02 09:28:39 +0100 |
| commit | f31caf61be87850f3afcd367d6eb9521b2f613da (patch) | |
| tree | 2bcceeb702ef0d35bfdc925977797c40290b6966 /README.md | |
| download | deepmind-qa-f31caf61be87850f3afcd367d6eb9521b2f613da.tar.gz deepmind-qa-f31caf61be87850f3afcd367d6eb9521b2f613da.zip | |
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diff --git a/README.md b/README.md new file mode 100644 index 0000000..63f8142 --- /dev/null +++ b/README.md @@ -0,0 +1,21 @@ +DeepMind : Teaching Machines to Read and Comprehend +========================================= + +This repository contains an implementation of the two models (the Deep LSTM and the Attentive Reader) described in *Teaching Machines to Read and Comprehend* by Karl Moritz Hermann and al., NIPS, 2015. This repository also contains an implementation of a Deep Bidirectional LSTM. + +Models are implemented using [Theano](https://github.com/Theano/Theano) and [Blocks](https://github.com/mila-udem/blocks). Datasets are implemented using [Fuel](https://github.com/mila-udem/fuel). + +The corresponding dataset is provided by [DeepMind](https://github.com/deepmind/rc-data) but if the script does not work you can check [http://cs.nyu.edu/~kcho/DMQA/](http://cs.nyu.edu/~kcho/DMQA/) by [Kyunghyun Cho](http://www.kyunghyuncho.me/). + +Reference +========= +[Teaching Machines to Read and Comprehend](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf), by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman and Phil Blunsom, Neural Information Processing Systems, 2015. + + +Credits +======= +[Thomas Mesnard](https://github.com/thomasmesnard) + +[Alex Auvolat](https://github.com/Alexis211) + +[Étienne Simon](https://github.com/ejls)
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