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Jonas Köhler
Jonas Köhler
Microsoft Research
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
Spherical CNNs
TS Cohen*, M Geiger*, J Köhler*, M Welling
International Conference on Learning Representations (ICLR), 2018
8182018
Boltzmann generators-sampling equilibrium states of many-body systems with deep learning
F Noé*, S Olsson*, J Köhler*, H Wu
Science 365 (6457), 2019
3912019
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
J Köhler*, L Klein*, F Noé
International Conference on Machine Learning (ICML), 2020
119*2020
Stochastic Normalizing Flows
H Wu, J Köhler, F Noé
Advances in Neural Information Processing Systems (NeurIPS), 2020
722020
Cross-Domain Mining of Argumentative Text through Distant Supervision
K Al-Khatib, H Wachsmuth, M Hagen, J Köhler, B Stein
NAACL-HLT, 2016
572016
Smooth Normalizing Flows
J Köhler*, A Krämer*, F Noé
Advances in Neural Information Processing Systems (NeurIPS), 2021
152021
Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics without Forces
J Köhler, Y Chen, A Krämer, C Clementi, F Noé
Journal of Chemical Theory and Computation, 2022
112022
Generating stable molecules using imitation and reinforcement learning
SA Meldgaard, J Köhler, HL Mortensen, MPV Christiansen, F Noé, ...
Machine Learning: Science and Technology 3 (1), 015008, 2021
102021
Training Neural Networks with Property-Preserving Parameter Perturbations
A Krämer, J Köhler, F Noé
NeurIPS workshop on Machine Learning and the Physical Sciences, 2020
2*2020
Rigid body flows for sampling molecular crystal structures
J Köhler, M Invernizzi, P de Haan, F Noé
arXiv preprint arXiv:2301.11355, 2023
2023
Optimal lossy compression for differentially private data release
J Köhler
Informatics Institute, University of Amsterdam, 2018
2018
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning
F Harder, J Köhler, M Welling, M Park
NeurIPS workshop on Privacy Preserving Machine Learning (PPML), 2018
2018
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Articles 1–12