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
4742018
Boltzmann generators-sampling equilibrium states of many-body systems with deep learning
F Noé, S Olsson, J Köhler, H Wu
Science 365 (6457), 2019
2312019
Convolutional Networks for Spherical Signals
T Cohen, M Geiger, J Köhler, M Welling
ICML workshop on Principled Approaches to Deep Learning (PADL), 2017
572017
Cross-Domain Mining of Argumentative Text through Distant Supervision
K Al-Khatib, H Wachsmuth, M Hagen, J Köhler, B Stein
NAACL-HLT, 2016
442016
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
J Köhler, L Klein, F Noé
International Conference on Machine Learning (ICML), 2020
232020
Equivariant flows: sampling configurations for multi-body systems with symmetric energies
J Köhler, L Klein, F Noé
arXiv preprint arXiv:1910.00753, 2019
232019
Stochastic Normalizing Flows
H Wu, J Köhler, F Noé
Advances in Neural Information Processing Systems (NeurIPS), 2020
202020
Generating stable molecules using imitation and reinforcement learning
SA Meldgaard, J Köhler, HL Mortensen, MPV Christiansen, F Noé, ...
arXiv preprint arXiv:2107.05007, 2021
32021
Training Invertible Linear Layers through Rank-One Perturbations
A Krämer, J Köhler, F Noé
arXiv preprint arXiv:2010.07033, 2020
22020
Smooth Normalizing Flows
J Köhler, A Krämer, F Noé
arXiv preprint arXiv:2110.00351, 2021
2021
Generating stable molecules using imitation and reinforcement learning
S Ager Meldgaard, J Köhler, HL Mortensen, MPV Christiansen, F Noé, ...
arXiv e-prints, arXiv: 2107.05007, 2021
2021
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
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