Nicholas Krämer

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 16

Top Topics

Differential Equations

State Space Model

Image Denoising

Dynamical Systems

Top Venues

J. Comput. Neurosci.

Publications

Nicholas Krämer, Pablo Moreno-Muñoz, Hrittik Roy, Søren Hauberg
Gradients of Functions of Large Matrices. CoRR (2024)
Nicholas Krämer, Jonathan Schmidt, Philipp Hennig
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. AISTATS (2022)
Jonathan Oesterle, Nicholas Krämer, Philipp Hennig, Philipp Berens
Correction to: Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. J. Comput. Neurosci. 51 (3) (2022)
Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR (2022)
Jonathan Oesterle, Nicholas Krämer, Philipp Hennig, Philipp Berens
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. J. Comput. Neurosci. 50 (4) (2022)
Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig
Probabilistic ODE Solutions in Millions of Dimensions. ICML (2022)
Nicholas Krämer, Philipp Hennig
Linear-Time Probabilistic Solution of Boundary Value Problems. NeurIPS (2021)
Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig
ProbNum: Probabilistic Numerics in Python. CoRR (2021)
Nicholas Krämer, Philipp Hennig
Linear-Time Probabilistic Solutions of Boundary Value Problems. CoRR (2021)
Jonathan Schmidt, Nicholas Krämer, Philipp Hennig
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. NeurIPS (2021)
Nicholas Krämer, Jonathan Schmidt, Philipp Hennig
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. CoRR (2021)
Jonathan Schmidt, Nicholas Krämer, Philipp Hennig
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. CoRR (2021)
Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig
Probabilistic ODE Solutions in Millions of Dimensions. CoRR (2021)
Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. CoRR (2020)
Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. ICML (2020)
Nicholas Krämer, Philipp Hennig
Stable Implementation of Probabilistic ODE Solvers. CoRR (2020)