フォロー
Nicholas Krämer
タイトル
引用先
引用先
年
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
232020
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data
J Schmidt, N Krämer, P Hennig
Advances in Neural Information Processing Systems 34, 12374-12385, 2021
212021
ProbNum: Probabilistic Numerics in Python
J Wenger, N Krämer, M Pförtner, J Schmidt, N Bosch, N Effenberger, ...
arXiv preprint arXiv:2112.02100, 2021
172021
Stable Implementation of Probabilistic ODE Solvers
N Krämer, P Hennig
Journal of Machine Learning Research 25 (111), 1-29, 2024
16*2024
Probabilistic ODE Solutions in Millions of Dimensions
N Krämer, N Bosch, J Schmidt, P Hennig
International Conference on Machine Learning, 11634-11649, 2022
162022
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations
N Krämer, J Schmidt, P Hennig
International Conference on Artificial Intelligence and Statistics, 625-639, 2022
122022
Linear-Time Probabilistic Solutions of Boundary Value Problems
N Krämer, P Hennig
Advances in Neural Information Processing Systems 34, 2021
82021
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig
arXiv preprint arXiv:2208.01565, 2022
72022
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models
J Oesterle, N Krämer, P Hennig, P Berens
Journal of Computational Neuroscience 50 (4), 485-503, 2022
3*2022
Gradients of Functions of Large Matrices
N Krämer, P Moreno-Muñoz, H Roy, S Hauberg
arXiv preprint arXiv:2405.17277, 2024
2024
Implementing Probabilistic Numerical Solvers for Differential Equations
PN Krämer
Universität Tübingen, 2024
2024
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