On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows

We present a novel theoretical framework for understanding the expressive power of normalizing flows. Despite their prevalence in scientific applications, a comprehensive understanding of flows remains elusive due to their restricted architectures. Existing theorems fall short as they require the us...

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Hauptverfasser:	Draxler, Felix, Wahl, Stefan, Schnörr, Christoph, Köthe, Ullrich
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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