no code implementations • 22 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.
no code implementations • 25 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.
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.
1 code implementation • 17 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.
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.