| SchNetPack: A deep learning toolbox for atomistic systems KT Schütt, P Kessel, M Gastegger, KA Nicoli, A Tkatchenko, KR Müller Journal of chemical theory and computation 15 (1), 448-455, 2018 | 391 | 2018 |
| Explanations can be manipulated and geometry is to blame AK Dombrowski, M Alber, C Anders, M Ackermann, KR Müller, P Kessel Advances in neural information processing systems 32, 2019 | 346 | 2019 |
| Higher spin interactions in four-dimensions: Vasiliev versus Fronsdal N Boulanger, P Kessel, E Skvortsov, M Taronna Journal of Physics A: Mathematical and Theoretical 49 (9), 095402, 2016 | 120 | 2016 |
| Asymptotically unbiased estimation of physical observables with neural samplers KA Nicoli, S Nakajima, N Strodthoff, W Samek, KR Müller, P Kessel Physical Review E 101 (2), 023304, 2020 | 102 | 2020 |
| Estimation of thermodynamic observables in lattice field theories with deep generative models KA Nicoli, CJ Anders, L Funcke, T Hartung, K Jansen, P Kessel, ... Physical review letters 126 (3), 032001, 2021 | 101 | 2021 |
| Fairwashing explanations with off-manifold detergent C Anders, P Pasliev, AK Dombrowski, KR Müller, P Kessel International Conference on Machine Learning, 314-323, 2020 | 95 | 2020 |
| Towards robust explanations for deep neural networks AK Dombrowski, CJ Anders, KR Müller, P Kessel Pattern Recognition 121, 108194, 2022 | 69 | 2022 |
| Higher spins and matter interacting in dimension three P Kessel, GL Gómez, E Skvortsov, M Taronna Journal of High Energy Physics 2015 (11), 1-107, 2015 | 38 | 2015 |
| Metric-and frame-like higher-spin gauge theories in three dimensions S Fredenhagen, P Kessel Journal of Physics A: Mathematical and Theoretical 48 (3), 035402, 2014 | 36 | 2014 |
| Cubic interactions of massless bosonic fields in three dimensions. II. Parity-odd and Chern-Simons vertices P Kessel, K Mkrtchyan Physical Review D 97 (10), 106021, 2018 | 33 | 2018 |
| Learning trivializing gradient flows for lattice gauge theories S Bacchio, P Kessel, S Schaefer, L Vaitl Physical Review D 107 (5), L051504, 2023 | 22 | 2023 |
| Gradients should stay on path: better estimators of the reverse-and forward KL divergence for normalizing flows L Vaitl, KA Nicoli, S Nakajima, P Kessel Machine Learning: Science and Technology 3 (4), 045006, 2022 | 18 | 2022 |
| Detecting and mitigating mode-collapse for flow-based sampling of lattice field theories KA Nicoli, CJ Anders, T Hartung, K Jansen, P Kessel, S Nakajima Physical Review D 108 (11), 114501, 2023 | 15 | 2023 |
| Diffeomorphic explanations with normalizing flows AK Dombrowski, JE Gerken, P Kessel ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2021 | 15 | 2021 |
| A machine-learning-based surrogate model of Mars’ thermal evolution S Agarwal, N Tosi, D Breuer, S Padovan, P Kessel, G Montavon Geophysical Journal International 222 (3), 1656-1670, 2020 | 13 | 2020 |
| The very basics of higher-spin theory P Kessel arXiv preprint arXiv:1702.03694, 2017 | 12 | 2017 |
| Path-gradient estimators for continuous normalizing flows L Vaitl, KA Nicoli, S Nakajima, P Kessel International conference on machine learning, 21945-21959, 2022 | 10 | 2022 |
| Deep learning for surrogate modeling of two-dimensional mantle convection S Agarwal, N Tosi, P Kessel, D Breuer, G Montavon Physical Review Fluids 6 (11), 113801, 2021 | 10 | 2021 |
| Toward constraining Mars' thermal evolution using machine learning S Agarwal, N Tosi, P Kessel, S Padovan, D Breuer, G Montavon Earth and Space Science 8 (4), e2020EA001484, 2021 | 10 | 2021 |
| Machine learning of thermodynamic observables in the presence of mode collapse KA Nicoli, C Anders, L Funcke, T Hartung, K Jansen, P Kessel, ... arXiv preprint arXiv:2111.11303, 2021 | 9 | 2021 |
