Scalable Normalizing Flows for Permutation Invariant Densities

Marin Biloš, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

6 Zitate (Scopus)

Abstract

Modeling sets is an important problem in machine learning since this type of data can be found in many domains. A promising approach defines a family of permutation invariant densities with continuous normalizing flows. This allows us to maximize the likelihood directly and sample new realizations with ease. In this work, we demonstrate how calculating the trace, a crucial step in this method, raises issues that occur both during training and inference, limiting its practicality. We propose an alternative way of defining permutation equivariant transformations that give closed form trace. This leads not only to improvements while training, but also to better final performance. We demonstrate the benefits of our approach on point processes and general set modeling.

OriginalspracheEnglisch
TitelProceedings of the 38th International Conference on Machine Learning, ICML 2021
Herausgeber (Verlag)ML Research Press
Seiten957-967
Seitenumfang11
ISBN (elektronisch)9781713845065
PublikationsstatusVeröffentlicht - 2021
Veranstaltung38th International Conference on Machine Learning, ICML 2021 - Virtual, Online
Dauer: 18 Juli 202124 Juli 2021

Publikationsreihe

NameProceedings of Machine Learning Research
Band139
ISSN (elektronisch)2640-3498

Konferenz

Konferenz38th International Conference on Machine Learning, ICML 2021
OrtVirtual, Online
Zeitraum18/07/2124/07/21

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