Jonas Köhler

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Noe FrankMicrosoft ResearchEmail verificata su microsoft.com
Max WellingCAIO CuspAI & Professor Machine Learning, University of AmsterdamEmail verificata su uva.nl
Taco CohenQualcomm AI ResearchEmail verificata su qti.qualcomm.com
Mario GeigerMITEmail verificata su mit.edu
Hao WuFree university of BerlinEmail verificata su zedat.fu-berlin.de
Andreas KrämerD. E. Shaw ResearchEmail verificata su DEShawResearch.com
Leon KleinFreie Universität BerlinEmail verificata su fu-berlin.de
Simon OlssonChalmers University of TechnologyEmail verificata su chalmers.se
Khalid Al-KhatibUniversity of GroningenEmail verificata su rug.nl
Henning WachsmuthLeibniz University Hannover, L3S Research CenterEmail verificata su ai.uni-hannover.de
Matthias HagenFriedrich-Schiller-Universität JenaEmail verificata su uni-jena.de
Benno SteinProfessor for Computer Science, Bauhaus-Universität WeimarEmail verificata su uni-weimar.de
Cecilia ClementiProfessor of Physics, Freie Universität BerlinEmail verificata su fu-berlin.de
Yaoyi ChenFreie Universität BerlinEmail verificata su fu-berlin.de
Bjørk HammerAarhusEmail verificata su phys.au.dk
Pim de HaanCuspAIEmail verificata su cusp.ai
Michele InvernizziComputational Scientist, PeptoneEmail verificata su peptone.io
Zeno SchätzlePhD student, FU Berlin, Microsoft ResearchEmail verificata su fu-berlin.de
Derk P. KooiPhD Student, Theoretical Chemistry VUEmail verificata su vu.nl
Klaas GiesbertzUniversitair Docent, Vrije UniversiteitEmail verificata su vu.nl

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Jonas Köhler

Altri nomiJonas Koehler

CuspAI

Email verificata su cusp.ai - Home page

Machine Learning AI for Science


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Spherical CNNs TS Cohen, M Geiger, J Köhler*, M Welling International Conference on Learning Representations (ICLR), 2018	1172	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	709	2019
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities J Köhler, L Klein, F Noé International Conference on Machine Learning (ICML), 2020	242	2020
Stochastic Normalizing Flows H Wu, J Köhler, F Noé Advances in Neural Information Processing Systems (NeurIPS), 2020	180	2020
Equivariant flows: sampling configurations for multi-body systems with symmetric energies J Köhler, L Klein, F Noé arXiv preprint arXiv:1910.00753, 2019	80	2019
Cross-Domain Mining of Argumentative Text through Distant Supervision K Al-Khatib, H Wachsmuth, M Hagen, J Köhler, B Stein NAACL-HLT, 2016	75	2016
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	61	2022
Smooth Normalizing Flows J Köhler, A Krämer, F Noé Advances in Neural Information Processing Systems (NeurIPS), 2021	57	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	21	2021
Rigid body flows for sampling molecular crystal structures J Köhler, M Invernizzi, P de Haan, F Noé International Conference on Machine Learning (ICML), 2023	18	2023
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
Highly Accurate Real-space Electron Densities with Neural Networks L Cheng, PB Szabó, Z Schätzle, DP Kooi, J Köhler, KJH Giesbertz, F Noé, ... arXiv preprint arXiv:2409.01306, 2024	2	2024
Machine Learning Driven Simulations of SARS-CoV-2 Fitness Landscape AEP Durumeric, J Köhler, K Elez, L Raich, PA Suriana, T Sztain bioRxiv, 2024.09. 20.614179, 2024		2024
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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