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Title: Equivariant Manifold Flows
Tractably modelling distributions over manifolds has long been an important goal in the natural sciences. Recent work has focused on developing general machine learning models to learn such distributions. However, for many applications these distributions must respect manifold symmetries—a trait which most previous models disregard. In this paper, we lay the theoretical foundations for learning symmetry-invariant distributions on arbitrary manifolds via equivariant manifold flows. We demonstrate the utility of our approach by learning quantum field theory-motivated invariant SU(n) densities and by correcting meteor impact dataset bias.  more » « less
Award ID(s):
2008102
PAR ID:
10387209
Author(s) / Creator(s):
Editor(s):
Ranzato, M.; Beygelzimer, A.; Dauphin, Y.; Liang, P.S.; Wortman Vaughan, J.
Date Published:
Journal Name:
Advances in neural information processing systems
Volume:
34
ISSN:
1049-5258
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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