text: The paper proposes a VAE inference network for a non-parametric topic model. The model on page 4 is confusing to me  since this is a topic model,  so document-specific topic distributions are required,  but what is shown is only stick-breaking for a mixture model. From what I can tell, the model itself is not new, only the fact that a VAE is used to approximate the posterior.  In this case, if the model is nonparametric, then comparing with Wang, et al (2011) seems the most relevant non-deep approach.  Given the factorization used in that paper, the q distributions are provably optimal by the standard method.  Therefore, something must be gained by the VAE due to a non-factorized q.  This would be best shown by comparing with the corresponding non-deep version of the model rather than LDA and other deep models.