A Additional Background on Bayesian neural networks and variational inference Consider a training set comprising of N input-output pairs, D = { x

Neal, 2012, Blundell et al., 2015], and (iii) using structured variational approximations that can potentially capture weight correlations in the posterior [Louizos and Welling, 2016, Zhang et al., We also vary the amount of inducing points we afford each kernel. The main difference in the local model is the dependence of weights on inputs.