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Rule extraction from deep neural nets

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Deep neural nets are a black box approach to statistical learning. The classification from the model is hard to interpret. We present an approach to extracting rules from a deep learning model that gets cancer patients’ gene expression as input and classifies them as those likely or unlikely to relapse. In this way we get some level of explainability of the output from the deep learning model.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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