General Techniques for Explaining/Interpreting Deep Neural Networks 8a.004.UVA


Project Start Date: Aug 1, 2019
Funding: Member Funded
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Project Summary

  • Much of the recent advancement in machine learning has been through neural networks (NNs), and in particular deep learning (DL) has shown record-breaking performance across many domains
  • In some fields where there is a human element involved, e.g. in medicine and finance, there can be legal or ethical considerations that require decisions to be interpretable/explainable. This requirement often prohibits the use of NNs and other so-called ‘black-box’ models.
  • There have been some proposed techniques to help explain the internal workings of NNs, many focused on image/visual recognition.
  • One of these methods known as feature occlusion shows promise for non-image and multi-modal data.
  • We propose to investigate the use of feature occlusion and potentially develop other techniques for use as general methods for explaining and interpreting deep learning models.

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