Model Selection for Bayesian Autoencoders: Supplementary Material Ba-Hien Tran EURECOM (France) Simone Rossi

In this section, we review some key results on the Wasserstein distance. The formulation in Eq. 6 is obtained by employing We use a single multi layer perceptron (MLP) layer with normalized output as the h function. Calculating the Wasserstein distance with the empirical distribution function is computationally attractive. Metropolis steps to accommodate numerical errors stemming from the integration. F .1 Experimental environment In our experiments, we use 4 workstations, which have the following specifications: GPU: NVIDIA Tesla P100 PCIe 16 GB.