Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks

arXiv:2403.13740v1 Announce Type: new
Abstract: The lack of transparency of Deep Neural Networks continues to be a limitation that severely undermines their reliability and usage in high-stakes applications. Promising approaches to overcome such limitations are Prototype-Based Self-Explainable Neural Networks (PSENNs), whose predictions rely on the similarity between the input at hand and a set of prototypical representations of the output classes, offering therefore a deep, yet transparent-by-design, architecture. So far, such models have been designed by considering pointwise estimates for …

abstract applications arxiv cs.lg limitations networks neural networks predictions reliability through transparency type uncertainty usage