Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. - Hans Kersting; Nicholas Krämer; Martin Schiegg; Christian Daniel; Michael Tiemann 001; Philipp Hennig (ICML, 2020)

When are Neural ODE Solutions Proper ODEs? - Katharina Ott; Prateek Katiyar; Philipp Hennig; Michael Tiemann 001 (CoRR, 2020)

ResNet After All: Neural ODEs and Their Numerical Solution. - Katharina Ott; Prateek Katiyar; Philipp Hennig; Michael Tiemann 001 (ICLR, 2021)

Symplectic Gaussian Process Dynamics. - Katharina Ensinger; Friedrich Solowjow; Michael Tiemann 001; Sebastian Trimpe (CoRR, 2021)

Structure-Preserving Gaussian Process Dynamics. - Katharina Ensinger; Friedrich Solowjow; Sebastian Ziesche; Michael Tiemann 001; Sebastian Trimpe (ECML/PKDD (5), 2022)

Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). - David Duvenaud; Markus Heinonen; Michael Tiemann 001; Max Welling (Dagstuhl Reports, 2022)

Combining Slow and Fast: Complementary Filtering for Dynamics Learning. - Katharina Ensinger; Sebastian Ziesche; Barbara Rakitsch; Michael Tiemann 001; Sebastian Trimpe (AAAI, 2023)

Baysian numerical integration with neural networks. - Katharina Ott; Michael Tiemann 001; Philipp Hennig; François-Xavier Briol (UAI, 2023)

Bayesian Numerical Integration with Neural Networks. - Katharina Ott; Michael Tiemann 001; Philipp Hennig; François-Xavier Briol (CoRR, 2023)

Uncertainty and Structure in Neural Ordinary Differential Equations. - Katharina Ott; Michael Tiemann 001; Philipp Hennig (CoRR, 2023)