Search Results for author:
Found 5 papers, 1 papers with code
Fast and Robust Shortest Paths on Manifolds Learned from Data
We propose a fast, simple and robust algorithm for computing shortest paths and distances on Riemannian manifolds learned from data.
Bayesian Filtering for ODEs with Bounded Derivatives
Recently there has been increasing interest in probabilistic solvers for ordinary differential equations (ODEs) that return full probability measures, instead of point estimates, over the solution and can incorporate uncertainty over the ODE at hand, e. g. if the vector field or the initial value is only approximately known or evaluable.
Dynamic Time-Of-Flight
Operating in this mode the camera essentially forgets all information previously captured - and performs depth inference from scratch for every frame.
A probabilistic model for the numerical solution of initial value problems
Like many numerical methods, solvers for initial value problems (IVPs) on ordinary differential equations estimate an analytically intractable quantity, using the results of tractable computations as inputs.
Probabilistic ODE Solvers with Runge-Kutta Means
We construct a family of probabilistic numerical methods that instead return a Gauss-Markov process defining a probability distribution over the ODE solution.
