Visualizing the diversity of representations learned by Bayesian neural networks. (arXiv:2201.10859v1 [cs.LG])

Explainable artificial intelligence (XAI) aims to make learning machines less
opaque, and offers researchers and practitioners various tools to reveal the
decision-making strategies of neural networks. In this work, we investigate how
XAI methods can be used for exploring and visualizing the diversity of feature
representations learned by Bayesian neural networks (BNNs). Our goal is to
provide a global understanding of BNNs by making their decision-making
strategies a) visible and tangible through feature visualizations and b)
quantitatively measurable with a …

arxiv bayesian diversity networks neural networks