Michael Tiemann (né Schober)
Michael Tiemann (né Schober)
Research scientist, Bosch Center for Artificial Intelligence
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Cited by
Cited by
Probabilistic ODE solvers with Runge-Kutta means
M Schober, DK Duvenaud, P Hennig
Advances in Neural Information Processing Systems, 739-747, 2014
A probabilistic model for the numerical solution of initial value problems
M Schober, S Särkkä, P Hennig
Statistics and Computing, 2018
Fast and robust shortest paths on manifolds learned from data
G Arvanitidis, S Hauberg, P Hennig, M Schober
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Temporal time-of-flight
A Adam, S Nowozin, O Yair, S Mazor, M Schober
US Patent App. 15/015,065, 2017
Resnet after all: Neural odes and their numerical solution
K Ott, P Katiyar, P Hennig, M Tiemann
International Conference on Learning Representations, 2020
Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers
M Schober, N Kasenburg, A Feragen, P Hennig, S Hauberg
International Conference on Medical Image Computing and Computer-Assisted …, 2014
Differentiable likelihoods for fast inversion of’likelihood-free’dynamical systems
H Kersting, N Krämer, M Schiegg, C Daniel, M Tiemann, P Hennig
International Conference on Machine Learning, 5198-5208, 2020
A random Riemannian metric for probabilistic shortest-path tractography
S Hauberg, M Schober, M Liptrot, P Hennig, A Feragen
International Conference on Medical Image Computing and Computer-Assisted …, 2015
GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver
D John, V Heuveline, M Schober
International Conference on Machine Learning, 3152-3162, 2019
Dynamic Time-of-Flight
M Schober, A Adam, O Yair, S Mazor, S Nowozin
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
When are Neural ODE Solutions Proper ODEs?
K Ott, P Katiyar, P Hennig, M Tiemann
arXiv preprint arXiv:2007.15386, 2020
Bayesian Filtering for ODEs with Bounded Derivatives
E Magnani, H Kersting, M Schober, P Hennig
arXiv preprint arXiv:1709.08471, 2017
Contributed Discussion on Article by Chkrebtii, Campbell, Calderhead, and Girolami
FX Briol, J Cockayne, O Teymur, WW Yoo, M Schober, P Hennig
Bayesian Analysis 11 (4), 1285-1293, 2016
Symplectic Gaussian Process Dynamics
K Ensinger, F Solowjow, M Tiemann, S Trimpe
arXiv preprint arXiv:2102.01606, 2021
Probabilistic Ordinary Differential Equation Solvers-Theory and Applications
M Schober
Universität Tübingen, 2019
Supplementary Materials for “Dynamic Time-of-Flight”
M Schober, A Adam, O Yair, S Mazor, S Nowozin
Using label metrics for Distance Metric Learning
M Schober
Camera-specific Image Denoising
M Schober
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