Jonas Köhler

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Noe FrankFU Berlin, GermanyVerified email at fu-berlin.de
Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
Taco CohenQualcomm AI ResearchVerified email at qti.qualcomm.com
Mario GeigerÉcole polytechnique fédérale de LausanneVerified email at epfl.ch
Hao WuFree university of BerlinVerified email at zedat.fu-berlin.de
Simon OlssonAssistant Professor, Chalmers University of TechnologyVerified email at chalmers.se
Leon KleinFreie Universität BerlinVerified email at fu-berlin.de
Andreas KrämerPostDoc, Freie Universität BerlinVerified email at fu-berlin.de
Khalid Al-KhatibUniversity of GroningenVerified email at rug.nl
Henning WachsmuthPaderborn UniversityVerified email at uni-paderborn.de
Matthias HagenMartin-Luther-Universität Halle-WittenbergVerified email at informatik.uni-halle.de
Benno SteinProfessor for Computer Science, Bauhaus-Universität WeimarVerified email at uni-weimar.de
Bjørk HammerAarhusVerified email at phys.au.dk
Mijung ParkUniversity of British ColumbiaVerified email at cs.ubc.ca
Frederik HarderMax Planck Institute for Intelligent Systems & University of Tübingen & International Max Planck Research School for Intelligent Systems (IMPRS-IS)Verified email at tuebingen.mpg.de
Cecilia ClementiProfessor of Physics, Freie Universität BerlinVerified email at fu-berlin.de

Jonas Köhler

Freie Universität Berlin

Verified email at fu-berlin.de - Homepage

Machine Learning Normalizing Flows Generative Models


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Spherical CNNs TS Cohen, M Geiger, J Köhler*, M Welling International Conference on Learning Representations (ICLR), 2018	608	2018
Boltzmann generators-sampling equilibrium states of many-body systems with deep learning F Noé, S Olsson, J Köhler*, H Wu Science 365 (6457), 2019	297	2019
Convolutional Networks for Spherical Signals T Cohen, M Geiger, J Köhler, M Welling ICML workshop on Principled Approaches to Deep Learning (PADL), 2017	57	2017
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities J Köhler, L Klein, F Noé International Conference on Machine Learning (ICML), 2020	52	2020
Cross-Domain Mining of Argumentative Text through Distant Supervision K Al-Khatib, H Wachsmuth, M Hagen, J Köhler, B Stein NAACL-HLT, 2016	49	2016
Stochastic Normalizing Flows H Wu, J Köhler, F Noé Advances in Neural Information Processing Systems (NeurIPS), 2020	44	2020
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	37	2019
Smooth Normalizing Flows J Köhler, A Krämer, F Noé Advances in Neural Information Processing Systems (NeurIPS), 2021	6	2021
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	4	2021
Training invertible linear layers through rank-one perturbations A Krämer, J Köhler, F Noé arXiv preprint arXiv:2010.07033, 2020	2	2020
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

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