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Jonas Köhler
Jonas Köhler
Microsoft Research AI4Science
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
10992018
Boltzmann generators-sampling equilibrium states of many-body systems with deep learning
F Noé*, S Olsson*, J Köhler*, H Wu
Science 365 (6457), 2019
6222019
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
J Köhler*, L Klein*, F Noé
International Conference on Machine Learning (ICML), 2020
1842020
Stochastic Normalizing Flows
H Wu, J Köhler, F Noé
Advances in Neural Information Processing Systems (NeurIPS), 2020
1522020
Cross-Domain Mining of Argumentative Text through Distant Supervision
K Al-Khatib, H Wachsmuth, M Hagen, J Köhler, B Stein
NAACL-HLT, 2016
742016
Equivariant flows: sampling configurations for multi-body systems with symmetric energies
J Köhler, L Klein, F Noé
arXiv preprint arXiv:1910.00753, 2019
692019
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
462022
Smooth Normalizing Flows
J Köhler*, A Krämer*, F Noé
Advances in Neural Information Processing Systems (NeurIPS), 2021
452021
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
192021
Rigid body flows for sampling molecular crystal structures
J Köhler, M Invernizzi, P de Haan, F Noé
International Conference on Machine Learning (ICML), 2023
142023
Training Neural Networks with Property-Preserving Parameter Perturbations
A Krämer, J Köhler, F Noé
NeurIPS workshop on Machine Learning and the Physical Sciences, 2020
3*2020
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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