Reviews: A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

I have read the author response and other reviews and decided to keep my original score of 7. Summary: The paper proposes a family of priors for GANs and VAEs. These priors are mixtures of Gaussians with a large number of components but which can be represented using few number of learnable parameters using tensor ring decomposition. This family of priors enable efficient marginalization and conditioning. The method is applicable to both discrete and continuous latent variables. The method is extended to conditional generative modeling; in particular missing values in the conditioning variable can be marginalized out.