Riemannian Metric Learning via Optimal Transport
We introduce an optimal transport-based model for learning a metric tensor from cross-sectional samples of evolving probability measures on a common Riemannian manifold. We neurally parametrize the metric as a spatially-varying matrix field and efficiently optimize our model's objective using b...
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| Formato: | Tese |
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Massachusetts Institute of Technology
2023
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| Acesso em linha: | https://hdl.handle.net/1721.1/147268 https://orcid.org/0000-0001-8516-6189 |