Cooperative Neural Networks
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README.md results updated Oct 23, 2018
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README.md

Cooperative Neural Networks - CoNN

Cooperative Neural Networks(CoNN) : Exploiting prior independence structure for improved classification
(also known as Joint Constraint Networks).

This work is published in NIPS 2018 link.

I am working on a Blog to highlight the novelties and main contributions of this work.

Dependencies

The file 'main.py' is tested on the following

  • Python 3
  • Pytorch 0.2.0
  • Numpy 1.15.1
  • nltk (preprocessing text)
  • P100 GPUs

Running CoNN-sLDA for Multi-Domain Sentiment Dataset

Getting the data link:

$ wget http://www.cs.jhu.edu/~mdredze/datasets/sentiment/unprocessed.tar.gz
$ tar -xvzf unprocessed.tar.gz

Running the script:

$ python main.py

with the default argparse settings, I get roughly

Avg auc(5 fold CV) = 0.9213456403902894 with std dev = 0.007758676219792392

Development

Code

I will be updating the repo with the following additions

  • Script compatible with latest Pytorch version
  • Script for latest Tensorflow version

Contributing

Issues can be reported at issues section.

If you want to discuss or contribute, please feel free to drop a mail or raise an issue :)

Collaboration

I will be happy to discuss and collaborate, if you want to use CoNN or its variant for some other Graphical models!

License

CoNN is released under Apache License. You can read about our license at here