Fast and Robust Shortest Paths on Manifolds Learned from Data
international conference on artificial intelligence and statistics, 2019.
We propose a fast, simple and robust algorithm for computing shortest paths and distances on Riemannian manifolds learned from data. This amounts to solving a system of ordinary differential equations (ODEs) subject to boundary conditions. Here standard solvers perform poorly because they require well-behaved Jacobians of the ODE, and usu...More
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