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arXiv · 1901.08557

On Network Science and Mutual Information for Explaining Deep Neural Networks

Brian Davis·Umang Bhatt·Kartikeya Bhardwaj·Radu Marculescu·José M. F. Moura
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Abstract

In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforward networks. We show that efficiently approximating mutual information allows us to create an information measure that quantifies how much information flows between any two neurons of a deep learning model. To that end, we propose NIF, Neural Information Flow, a technique for codifying information flow that exposes deep learning model internals and provides feature attributions.

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Brian Davis, Umang Bhatt, Kartikeya Bhardwaj, Radu Marculescu, José M. F. Moura. 2019-01-20. On Network Science and Mutual Information for Explaining Deep Neural Networks. https://doi.org/10.1109/icassp40776.2020.9053078

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