We introduce a new benchmark for approximate Bayesian inference methods for deep neural networks. Specifically, we focus on Markov chain Monte Carlo approximate inference approaches such as HMC, SGHMC, and SGLD.
We introduce a new symmetric integration scheme for split HMC that enables us to perform full HMC inference over neural networks. The resulting models provide a better performance in both accuracy and uncertainty quantification compared to prior approximate inference approaches.