{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/marginal-tail-adaptive-normalizing-flows-1","title":"Marginal Tail-Adaptive Normalizing Flows","arxiv_id":"2206.10311","date":"2022-06-21","proceeding":null,"authors":["Mike Laszkiewicz","Johannes Lederer","Asja Fischer"],"abstract":"Learning the tail behavior of a distribution is a notoriously difficult problem. By definition, the number of samples from the tail is small, and deep generative models, such as normalizing flows, tend to concentrate on learning the body of the distribution. 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