Abstract
Normalizing Flows are generative models which produce tractable distributions
where both sampling and density evaluation can be efficient and exact. The goal
of this survey article is to give a coherent and comprehensive review of the
literature around the construction and use of Normalizing Flows for
distribution learning. We aim to provide context and explanation of the models,
review current state-of-the-art literature, and identify open questions and
promising future directions.
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