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MCL-GAN

TensorFlow ensembles for multiple-choice-learning.

This research focuses on encouraging useful diversity for Multiple Choice Learning (MCL) problems (see Stefan Lee's NIPS 2016 paper).

We plan to introduce several new methods that represent a paradigm shift for handling MCL, or M-best, problems. Rather than training an ensemble of models using a diversity loss term, our approaches will focus model attention on different parts of the input space.

Structure of Repo:

  • Each folder represents a publication.
  • Within each folder you will find
    • Code
    • PDF of final publication
    • Jupyter Notebook Tutorial

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