| 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 | 251 | 2018 |
| Asymptotically unbiased estimation of physical observables with neural samplers KA Nicoli, S Nakajima, N Strodthoff, W Samek, KR Müller, P Kessel Phys. Rev. E 101 (2), 023304, 2020 | 63 | 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 | 52 | 2021 |
| Modern applications of machine learning in quantum sciences A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint arXiv:2204.04198, 2022 | 8 | 2022 |
| Comment on" Solving Statistical Mechanics Using VANs": Introducing saVANt-VANs Enhanced by Importance and MCMC Sampling KA Nicoli, P Kessel, N Strodthoff, W Samek, KR Müller, S Nakajima arXiv preprint arXiv:1903.11048, 2019 | 4 | 2019 |
| 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 | 2 | 2022 |
| Path-gradient estimators for continuous normalizing flows L Vaitl, KA Nicoli, S Nakajima, P Kessel International Conference on Machine Learning, 21945-21959, 2022 | 2 | 2022 |
| Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse KA Nicoli, C Anders, L Funcke, T Hartung, K Jansen, P Kessel, ... The 38th International Symposium on Lattice Field Theory, LATTICE2021, 2022 | 1 | 2022 |
| Analysis of Atomistic Representations Using Weighted Skip-Connections KA Nicoli, P Kessel, M Gastegger, KT Schütt arXiv preprint arXiv:1810.09751, 2018 | | 2018 |
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinEmail verificata su tu-berlin.de
