Bayesian neural networks with possibility theory

Abstract

In this paper, we introduce the use of possibility theory into bayesian neural networks to attempt to quantify uncertainty of neural network predictions. While there exist other methods which have their own quantification, they face certain constrains such as scalability or the use of parametric assumption. In the method we propose, we aim to rectify these constrains while still maintaining reasonable results for uncertainty quantification.