Reviews: A Simple Baseline for Bayesian Uncertainty in Deep Learning

The method is almost trivially simple, scalable and easy to implement, yet the empirical evaluation shows that it performs competitively and often better than all alternatives. This is the best kind of paper! The task of representing uncertainty over model weights is highly significant -- it is debatably *the* core problem in Bayesian deep learning, with (as the authors point out) applications to calibrated decision making, out-of-sample detection, adversarial robustness, transfer learning, and more. I expect this baseline to be widely used by researchers in the field, and likely implemented by practitioners as well. The paper is well written and easy to follow.