  There is a significant need for principled uncertainty reasoning in machine
learning systems as they are increasingly deployed in safety-critical domains.
A new approach with uncertainty-aware regression-based neural networks (NNs),
based on learning evidential distributions for aleatoric and epistemic
uncertainties, shows promise over traditional deterministic methods and typical
Bayesian NNs, notably with the capabilities to disentangle aleatoric and
epistemic uncertainties. Despite some empirical success of Deep Evidential
Regression (DER), there are important gaps in the mathematical foundation that
raise the question of why the proposed technique seemingly works. We detail the
theoretical shortcomings and analyze the performance on synthetic and
real-world data sets, showing that Deep Evidential Regression is a heuristic
rather than an exact uncertainty quantification. We go on to discuss
corrections and redefinitions of how aleatoric and epistemic uncertainties
should be extracted from NNs.
