Review for NeurIPS paper: On the Expressiveness of Approximate Inference in Bayesian Neural Networks

Additional Feedback: Section 2.2 does not seem like an important point for you to make. There is extensive prior work (as you acknowledge) on BNN priors and this is not core to your argument. It's probably enough to acknowledge somewhere in a sentence that you pick priors that are not perverse and create space for figures that are more important to your paper that have been moved to the appendix. That said, I do think you imply that your results for deeper networks are stronger than they actually are. I think there's a good chance that the effects you identify are much more pronounced for small numbers of datapoints in low-dimensional data.