Reviews: Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders

The paper proposes to introduce pair-copula construction in the autoencoder architecture to create more robust generative model. Specifically, with a conventionally trained autoencoder encoding input data into a low dimensional latent space, the authors propose estimating the encoding vector distribution using vine-copulas. It is claimed that such estimation can be done efficiently based on sequential estimation of the pair copulation decomposition on vine trees. Furthermore, the estimated distribution can be sampled easily and passed to the decoder to create new data, thus serve as a generative model. My biggest issue with the work is the presentation, which needs a lot of improvements.