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2020 – today
- 2024
[c9]Amon Lahr, Filip Tronarp, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig, Melanie N. Zeilinger:
Probabilistic ODE solvers for integration error-aware numerical optimal control. L4DC 2024: 1018-1032- 2023
- [c8]Jonathan Schmidt, Qadeer Khan, Daniel Cremers:
LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels. ITSC 2023: 2835-2842 - [c7]Jonathan Schmidt, Philipp Hennig, Jörg Nick, Filip Tronarp:
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions. NeurIPS 2023 - [i8]Jonathan Schmidt, Philipp Hennig, Jörg Nick, Filip Tronarp
:
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions. CoRR abs/2306.07774 (2023) - [i7]Jonathan Schmidt, Qadeer Khan, Daniel Cremers:
LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels. CoRR abs/2308.01424 (2023) - 2022
- [b1]Jonathan Schmidt:
Machine learning the thermodynamic stability of crystal structures. University of Halle, Germany, 2022 - [j1]Johannes Gedeon, Jonathan Schmidt, Matthew J. P. Hodgson, Jack Wetherell, Carlos L. Benavides-Riveros, Miguel A. L. Marques:
Machine learning the derivative discontinuity of density-functional theory. Mach. Learn. Sci. Technol. 3(1): 15011 (2022) - [c6]Nicholas Krämer, Jonathan Schmidt, Philipp Hennig:
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. AISTATS 2022: 625-639 - [c5]Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig:
Probabilistic ODE Solutions in Millions of Dimensions. ICML 2022: 11634-11649 - [i6]Jonathan Schmidt, Haichen Wang, Georg Schmidt, Miguel Marques:
Machine Learning guided high-throughput search of non-oxide garnets. CoRR abs/2208.13742 (2022) - [i5]Jonathan Schmidt, Noah Hoffmann, Haichen Wang, Pedro Borlido, Pedro J. M. A. Carriço, Tiago F. T. Cerqueira, Silvana Botti, Miguel A. L. Marques:
Large-scale machine-learning-assisted exploration of the whole materials space. CoRR abs/2210.00579 (2022) - 2021
- [c4]Jonathan Schmidt, Nicholas Krämer, Philipp Hennig:
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. NeurIPS 2021: 12374-12385 - [i4]Jonathan Schmidt, Nicholas Krämer, Philipp Hennig:
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. CoRR abs/2103.10153 (2021) - [i3]Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig:
Probabilistic ODE Solutions in Millions of Dimensions. CoRR abs/2110.11812 (2021) - [i2]Nicholas Krämer, Jonathan Schmidt, Philipp Hennig:
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. CoRR abs/2110.11847 (2021) - [i1]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 abs/2112.02100 (2021)
2010 – 2019
- 2019
- [c3]Benedikt Walter, Jan Martin, Jonathan Schmidt, Hanna Dettki, Stephan Rudolph:
Executable State Machines Derived from Structured Textual Requirements - Connecting Requirements and Formal System Design. MODELSWARD 2019: 193-200
2000 – 2009
- 2006
- [c2]Jonathan Schmidt:
Dynamic Port 25 Blocking to Control SPAM Zombies. CEAS 2006
1970 – 1979
- 1978
- [c1]Ted G. Lewis, Percy Wood, Jonathan Schmidt, Stuart E. Savory, Dale Johnson, Patricia A. Carr:
Small systems performance in business (Panel Session). SIGMINI 1978: 71-72
Coauthor Index
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