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...
Autor principal: | |
---|---|
Outros Autores: | |
Formato: | Tese |
Publicado em: |
Massachusetts Institute of Technology
2023
|
Acesso em linha: | https://hdl.handle.net/1721.1/147268 https://orcid.org/0000-0001-8516-6189 |