On Network Science and Mutual Information for Explaining Deep Neural Networks

Understand

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.