Review for NeurIPS paper: Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax

Weaknesses: * Experiments: While the proposed method outperformed existing approaches in the results presented, the experiments seem rather limited in scope. For example, the VAE experiments included in the main text were done using linear encoder/decoder, which is very rarely used in practice. This is particularly concerning because for the nonlinear experiment included in the appendix, GS outperformed IGR on FMNIST (see Table 1). Although this was the only dataset on which IGR didn't outperform GS, it does raise the question of how IGR would scales to more difficult tasks (e.g. In this light, it would really strengthen this paper if the authors could demonstrate that IGR outperforms GS on more challenging tasks as well as compared to other methods such as VIMCO [1] and VQ-VAE [2].