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Normalizing Flows: An Introduction and Review of Current Methods
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Normalizing Flows: An Introduction and Review of Current Methods
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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.
Forward citations
Cited by 12 Pith papers
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Normalizing Flows on Quotient Manifolds via Boundary Quotients
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Normalizing Flows on Quotient Manifolds via Boundary Quotients
Flows on the two solid tori of a lens space can approximate pushforwards of S3 densities onto L(p;q), and the construction deletes distribution symmetries before training.
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