DeepEvidentialRegression

Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper,we propose anovelmethod for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order tolearn both aleatoric andepistemic uncertainty.