Search Results for author: Michael Schober

Found 5 papers, 1 papers with code

Fast and Robust Shortest Paths on Manifolds Learned from Data

no code implementations22 Jan 2019 Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober

We propose a fast, simple and robust algorithm for computing shortest paths and distances on Riemannian manifolds learned from data.

Metric Learning

Bayesian Filtering for ODEs with Bounded Derivatives

no code implementations25 Sep 2017 Emilia Magnani, Hans Kersting, Michael Schober, Philipp Hennig

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

no code implementations CVPR 2017 Michael Schober, Amit Adam, Omer Yair, Shai Mazor, Sebastian Nowozin

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

1 code implementation17 Oct 2016 Michael Schober, Simo Särkkä, Philipp Hennig

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

no code implementations NeurIPS 2014 Michael Schober, David Duvenaud, Philipp Hennig

We construct a family of probabilistic numerical methods that instead return a Gauss-Markov process defining a probability distribution over the ODE solution.

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