  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.
