text: This paper addresses the question of unsupervised clustering with high classification performance. They propose a deep variational autoencoder architecture with categorical latent variables at the deepest layer and propose to train it with modifications of the standard variational approach with reparameterization gradients. The model is tested on a medical imagining dataset where the task is to distinguish healthy from pathological lymphocytes from blood samples. I am not an expert on this particular dataset, but to my eye the results look impressive. They show high sensitivity and high specificity. This paper may be an important contribution to the medical imaging community. My primary concern with the paper is the lack of novelty and relatively little in the way of contributions to the ICLR community. The proposed model is a simple variant on the standard VAE models (see for example the Ladder VAE https://arxiv.org/abs/1602.02282 for deep models with multiple stochastic layers). This would be OK if a thorough evaluation on at least two other datasets showed similar improvements as the lymphocytes dataset. As it stands, it is difficulty for me to assess the value of this model. Minor questions / concerns: - The authors claim in the first paragraph of 3.2 that deterministic mappings lack expressiveness. Would be great to see the paper take this claim seriously and investigate it. - In equation (13) it isn't clear whether you use q_phi to be the discrete mass or the concrete density. The distinction is discussed in https://arxiv.org/abs/1611.00712 - Would be nice to report the MCC in experimental results.