arXiv Machine Learning June 30, 2026 By Davide Valsecchi, Mauro Doneg\`a, Rainer Wallny

Factorizable Normalizing Flows for parameter-dependent density morphing

Read the original on arXiv Machine Learning →

arXiv:2606. 30489v1 Announce Type: cross Abstract: Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeling how that density deforms as a function of continuous parameters: the strength of a physical effect, a calibration constant, or a source of systematic uncertainty.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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