  We propose a new structure for the variational auto-encoders (VAEs) prior,
with the weakly informative multivariate Student's t-distribution. In the
proposed model all distribution parameters are trained, thereby allowing for a
more robust approximation of the underlying data distribution. We used
Fashion-MNIST data in two experiments to compare the proposed VAEs with the
standard Gaussian priors. Both experiments showed a better reconstruction of
the images with VAEs using Student's t-prior distribution.
