3 papers
cs.LG2024
Computing distances and means on manifolds with a metric-constrained Eikonal approach
Daniel Kelshaw, Luca Magri
Computing distances on Riemannian manifolds is a challenging problem with numerous applications, from physics, through statistics, to machine learning. In this paper, we introduce…
cs.CG2023
Manifold-augmented Eikonal Equations: Geodesic Distances and Flows on Differentiable Manifolds
Daniel Kelshaw, Luca Magri
Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and pr…
cs.LG2023
Short and Straight: Geodesics on Differentiable Manifolds
Daniel Kelshaw, Luca Magri
Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and pr…