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Poster
Riemannian Convex Potential Maps
samuel cohen · Brandon Amos · Yaron Lipman

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @ None #None
in Poster Session 1 »

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited by representational and computational tradeoffs. We propose and study a class of flows that uses convex potentials from Riemannian optimal transport. These are universal and can model distributions on any compact Riemannian manifold without requiring domain knowledge of the manifold to be integrated into the architecture. We demonstrate that these flows can model standard distributions on spheres, and tori, on synthetic and geological data.

Keywords: Generative Models · Deep Learning

Author Information

samuel cohen (University College London)
Brandon Amos (Facebook AI Research)
Yaron Lipman (Facebook AI Research)

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