Follow
Jonas Köhler
Title
Cited by
Cited by
Year
Spherical CNNs
TS Cohen*, M Geiger*, J Köhler*, M Welling
International Conference on Learning Representations (ICLR), 2018
6082018
Boltzmann generators-sampling equilibrium states of many-body systems with deep learning
F Noé*, S Olsson*, J Köhler*, H Wu
Science 365 (6457), 2019
2972019
Convolutional Networks for Spherical Signals
T Cohen, M Geiger, J Köhler, M Welling
ICML workshop on Principled Approaches to Deep Learning (PADL), 2017
572017
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
J Köhler*, L Klein*, F Noé
International Conference on Machine Learning (ICML), 2020
522020
Cross-Domain Mining of Argumentative Text through Distant Supervision
K Al-Khatib, H Wachsmuth, M Hagen, J Köhler, B Stein
NAACL-HLT, 2016
492016
Stochastic Normalizing Flows
H Wu, J Köhler, F Noé
Advances in Neural Information Processing Systems (NeurIPS), 2020
442020
Equivariant flows: sampling configurations for multi-body systems with symmetric energies
J Köhler*, L Klein*, F Noé
NeurIPS Workshop on Machine Learning and the Physical Sciences, 2019
372019
Smooth Normalizing Flows
J Köhler*, A Krämer*, F Noé
Advances in Neural Information Processing Systems (NeurIPS), 2021
62021
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
42021
Training invertible linear layers through rank-one perturbations
A Krämer, J Köhler, F Noé
arXiv preprint arXiv:2010.07033, 2020
22020
Force-matching Coarse-Graining without Forces
J Köhler, Y Chen, A Krämer, C Clementi, F Noé
arXiv preprint arXiv:2203.11167, 2022
2022
Training Neural Networks with Property-Preserving Parameter Perturbations
A Krämer, J Köhler, F Noé
NeurIPS workshop on Machine Learning and the Physical Sciences, 2020
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
Preserving Properties of Neural Networks by Perturbative Updates
A Krämer, J Köhler, F Noé
Boltzmann generators-sampling equilibrium states of many-body systems with deep learning Open Website
F Noe, S Olsson, J Köhler, H Wu
The system can't perform the operation now. Try again later.
Articles 1–16