Reviews: Iterative Refinement of the Approximate Posterior for Directed Belief Networks

The paper is very clearly written and describes technical concepts in a very comprehensible way. The approach is sound and well motivated and the experimental comparisons with other approaches are fair, though they could have been more extensive in terms of datasets. My greatest concern is about the execution time of the proposed approach, since this is a sequential Monte Carlo method that performs multiple refinement passes for each step of the training process. The authors report convergence curves vs epochs but not vs wall clock time, which should be provided as the main motivation of the paper is to speed up training for this class of generative methods. The experimental section is good in terms of which methods it compares against, but a bit lacking in terms of datasets.