Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Copula-Based Normalizing Flows   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.

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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an