  A simple approach to obtaining uncertainty-aware neural networks for
regression is to do Bayesian linear regression (BLR) on the representation from
the last hidden layer. Recent work [Riquelme et al., 2018, Azizzadenesheli et
al., 2018] indicates that the method is promising, though it has been limited
to homoscedastic noise. In this paper, we propose a novel variation that
enables the method to flexibly model heteroscedastic noise. The method is
benchmarked against two prominent alternative methods on a set of standard
datasets, and finally evaluated as an uncertainty-aware model in model-based
reinforcement learning. Our experiments indicate that the method is competitive
with standard ensembling, and ensembles of BLR outperforms the methods we
compared to.
