Paper:
The Variational Auto Encoder (VAE) is a popular generative latent variable model that is often applied for representation learning. Standard VAEs assume continuous valued latent variables and are trained by maximization of the evidence lower bound (ELBO). Conventional methods obtain a differentiable estimate of the ELBO with reparametrized sampling and optimize it with Stochastic Gradient Descend (SGD). However, this is not possible if we want to train VAEs with discrete valued latent variables, since reparametrized sampling is not possible. Till now, there exist no simple solutions to circumvent this problem. In this paper, we propose an easy method to train VAEs with binary or categorically valued latent representations. Therefore, we use a differentiable estimator for the ELBO which is based on importance sampling. In experiments, we verify the approach and train two different VAEs architectures with Bernoulli and Categorically distributed latent representations on two different benchmark datasets.

Review:
In computing the gradient of the ELBO, the main challenge lies in computing the gradient of the reconstruction loss with respect to the encoder parameters. VAEs traditionally rely on reparameterization in order to obtain a low-variance estimate, but there are a number of other gradient estimators that one can apply. The authors here proprose to use a trick that is known, but perhaps not widely known: If we introduce an importance sampling distribution, then we can use samples from this distribution to compute an importance-weighted estimate of the gradient. The idea is now that we can compute the gradient w.r.t. the encoder parameters as a simple importance-sampling estimate, which obviates then need for reparameterization, or likelihood-ratio estimators. The authors then apply this trick to train VAEs with discrete latent variables.
While I think that the idea that the authors present in this paper is worth further exploration, the paper in its current form is not sufficiently mature to appear at ICLR. The two areas where this paper would benefit from improvement are
1. Discussion of related work. 
While the authors seem to suggest that there has been no work on VAEs with discrete latent variables, there has in fact been quite a lot of work, including work on VAEs that contain both discrete and continuous variables (e.g. [8-10], but I'm almost certainly missing further references). There has also been a large body of work on continuous relaxations of discrete variables that are amenable to reparameterization (e.g. [6-7], and references therein). There has also been a line of work relating importance sampling to variational objectives (see [1-3] as key references). Finally, there is also related work on reweighted-wake-sleep style objectives (see [4]) which similarly don't require reparameterization. From what I can tell, none of these references are cited or discussed as related work. In order to place this work in context, I would rewrite 2 to discuss approaches to gradient estimation in this space, which then makes it much easier to explain how this approach differs. 
2. Empirical evaluation.
The authors only evaluate on MNIST and F-MNIST, and don't compare to any existing approaches. More than a couple of reconstructions, what I would like to see is an analysis of gradient variances, asymptotic ELBO estimates. I would also like to see a larger set of problems. Finally I would like to see a clear comparison to other methods based on, e.g., continuous relaxations. 
References
[1] Y. Burda, R. Grosse, and R. Salakhutdinov, “Importance Weighted Autoencoders,” arXiv:1509.00519 [cs, stat], Sep. 2015.
[2] T. Rainforth et al., “Tighter Variational Bounds are Not Necessarily Better,” arXiv:1802.04537 [cs, stat], Feb. 2018.
[3] G. Tucker, D. Lawson, S. Gu, and C. J. Maddison, “Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives,” arXiv:1810.04152 [cs, stat], Oct. 2018.
[4] T. A. Le, A. R. Kosiorek, N. Siddharth, Y. W. Teh, and F. Wood, “Revisiting Reweighted Wake-Sleep,” arXiv:1805.10469 [cs, stat], May 2018.
[5] A. Mnih and D. J. Rezende, “Variational inference for Monte Carlo objectives,” arXiv:1602.06725 [cs, stat], Feb. 2016.
[6] G. Tucker, A. Mnih, C. J. Maddison, J. Lawson, and J. Sohl-Dickstein, “REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models,” in Advances in Neural Information Processing Systems, 2017, pp. 2624–2633.
[7] W. Grathwohl, D. Choi, Y. Wu, G. Roeder, and D. Duvenaud, “Backpropagation through the Void: Optimizing control variates for black-box gradient estimation,” arXiv preprint arXiv:1711.00123, 2017.
[8] J. T. Rolfe, “Discrete Variational Autoencoders,” arXiv:1609.02200 [cs, stat], Sep. 2016.
[9] E. Dupont, “Learning Disentangled Joint Continuous and Discrete Representations,” arXiv:1804.00104 [cs, stat], Mar. 2018.
[10] B. Esmaeili et al., “Structured Disentangled Representations,” arXiv:1804.02086 [cs, stat], Apr. 2018.