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Copula-Based Normalizing Flows

2021-07-15 14:22:28
Mike Laszkiewicz, Johannes Lederer, Asja Fischer

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2107.07352

PDF

https://arxiv.org/pdf/2107.07352.pdf


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