PosteriorRefinementImprovesSampleEfficiency inBayesianNeuralNetworks

Its derivation, based on Lu et al.[54] is as follows. For the HMC baseline, we use the default implementation of NUTS in Pyro. In Table 7, we present the detailed, non-averaged results to complement Table 4. In both cases, we observe that the performance of the refined posterior approaches HMC's. C.2 Textclassification We further validate the proposed method on text classification problems.