  Conventional prior for Variational Auto-Encoder (VAE) is a Gaussian
distribution. Recent works demonstrated that choice of prior distribution
affects learning capacity of VAE models. We propose a general technique
(embedding-reparameterization procedure, or ER) for introducing arbitrary
manifold-valued variables in VAE model. We compare our technique with a
conventional VAE on a toy benchmark problem. This is work in progress.
