NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:3510
Title:Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders

This paper proposes a vine copula autoencoder to construct flexible generative models for high-dimensional, structured data in three steps. By exploiting vine copulas, the proposed approach can transform any already trained autoencoder into a flexible generative model at a low computational cost, and its good performance was nicely demonstrated. This is a nice contribution to the field of constructing deep generative models.