Michael Tiemann

According to our database, Michael Tiemann authored at least 13 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

BhashaSetu: A Data-Centric Approach to Low-Resource Machine Translation.

Param Thakkar, Anushka Yadav, Michael Tiemann, Abhi Mehta, Akshita Bhasin, Shrinivas Khedkar

CoRR, May, 2026

CORDS: Continuous Representations of Discrete Structures.

Tin Hadzi Veljkovic, Erik J. Bekkers, Michael Tiemann, Jan-Willem van de Meent

CoRR, January, 2026

2025

Large Language Models in Labor Market Research Data Management: Potentials and Limitations.

Jens Dörpinghaus, Michael Tiemann

Proceedings of the Workshop on Large Language Models for Research Data Management, 2025

2023

Uncertainty and Structure in Neural Ordinary Differential Equations.

Katharina Ott, Michael Tiemann, Philipp Hennig

CoRR, 2023

Bayesian Numerical Integration with Neural Networks.

Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol

CoRR, 2023

Baysian numerical integration with neural networks.

Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Combining Slow and Fast: Complementary Filtering for Dynamics Learning.

Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch, Michael Tiemann, Sebastian Trimpe

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332).

David Duvenaud, Markus Heinonen, Michael Tiemann, Max Welling

Dagstuhl Reports, 2022

Structure-Preserving Gaussian Process Dynamics.

Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche, Michael Tiemann, Sebastian Trimpe

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

2021

Symplectic Gaussian Process Dynamics.

Katharina Ensinger, Friedrich Solowjow, Michael Tiemann, Sebastian Trimpe

CoRR, 2021

ResNet After All: Neural ODEs and Their Numerical Solution.

Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

When are Neural ODE Solutions Proper ODEs?

Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann

CoRR, 2020

Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems.

Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig

Proceedings of the 37th International Conference on Machine Learning, 2020