  Normalizing flows, which learn a distribution by transforming the data to
samples from a Gaussian base distribution, have proven powerful density
approximations. But their expressive power is limited by this choice of the
base distribution. We, therefore, propose to generalize the base distribution
to a more elaborate copula distribution to capture the properties of the target
distribution more accurately. In a first empirical analysis, we demonstrate
that this replacement can dramatically improve the vanilla normalizing flows in
terms of flexibility, stability, and effectivity for heavy-tailed data. Our
results suggest that the improvements are related to an increased local
Lipschitz-stability of the learned flow.
