Reviews: Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo

Update after rebuttal: I think the rebuttal is fair. It is very reassuring that pseudocode will be provided to the readers. I therefore keep my decision unchanged. Original review: In the paper "Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo" the author(s) consider the problem of inference for deep gaussian processes (DGPs). Given the large number of layers and width of each layer, direct inference is computaitonal infeasible, which has motivated numerous variational inference methods to approximate the posterior distribution, for example doubly stochastic variational inference (DSVI) of [Salimbeni and Deisenroth, 2017] The authors argue that these unimodal approximations are typically poor given the multimodal and non-Gaussian nature of the posterior.