Nicholas Krämer - Google Scholar

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Citations	14	14
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202020213 11

Co-authors

Philipp HennigUniversity of Tübingen & Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
Hans KerstingSIERRA Team, INRIA ParisVerified email at inria.fr
Jonathan SchmidtMasters Student in Machine Learning, University of TübingenVerified email at student.uni-tuebingen.de

Nicholas Krämer

Nicholas Krämer

University of Tübingen

Verified email at uni-tuebingen.de - Homepage

(Bayesian) Inference Differential Equations Probabilistic Numerics


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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	10	2020
Stable Implementation of Probabilistic ODE Solvers N Krämer, P Hennig arXiv preprint arXiv:2012.10106, 2020	3	2020
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data J Schmidt, N Krämer, P Hennig arXiv preprint arXiv:2103.10153, 2021	1	2021
Linear-Time Probabilistic Solutions of Boundary Value Problems N Krämer, P Hennig arXiv preprint arXiv:2106.07761, 2021		2021
Numerical uncertainty can critically affect simulations of mechanistic models in neuroscience J Oesterle, N Kraemer, P Hennig, P Berens bioRxiv, 2021		2021

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