| Higher-order explanations of graph neural networks via relevant walks T Schnake, O Eberle, J Lederer, S Nakajima, KT Schütt, KR Müller, ... IEEE transactions on pattern analysis and machine intelligence 44 (11), 7581 …, 2021 | 280* | 2021 |
| Toward explainable artificial intelligence for regression models: A methodological perspective S Letzgus, P Wagner, J Lederer, W Samek, KR Müller, G Montavon IEEE Signal Processing Magazine 39 (4), 40-58, 2022 | 99 | 2022 |
| Machine learning–based charge transport computation for pentacene J Lederer, W Kaiser, A Mattoni, A Gagliardi Advanced Theory and Simulations 2 (2), 1800136, 2019 | 50 | 2019 |
| SchNetPack 2.0: A neural network toolbox for atomistic machine learning KT Schütt, SSP Hessmann, NWA Gebauer, J Lederer, M Gastegger The Journal of Chemical Physics 158 (14), 2023 | 40 | 2023 |
| Automatic identification of chemical moieties J Lederer, M Gastegger, KT Schütt, M Kampffmeyer, KR Müller, OT Unke Physical Chemistry Chemical Physics 25 (38), 26370-26379, 2023 | 7 | 2023 |
| Peering inside the black box: Learning the relevance of many-body functions in Neural Network potentials K Bonneau, J Lederer, C Templeton, D Rosenberger, KR Müller, ... arXiv preprint arXiv:2407.04526, 2024 | 1 | 2024 |
| Towards Symbolic XAI--Explanation Through Human Understandable Logical Relationships Between Features T Schnake, FR Jafaria, J Lederer, P Xiong, S Nakajima, S Gugler, ... arXiv preprint arXiv:2408.17198, 2024 | | 2024 |
| Machine learning for predicting charge transfer integrals in organic thin films M Rinderle, J Lederer, W Kaiser, A Gagliardi European Materials Research Society Spring Meeting, Presentation AA. 6.2, 2019 | | 2019 |
| Machine Learning Based Ab Initio Numerical Study of Charge Transport within Non-Crystalline Organic Semiconductors W Kaiser, J Lederer, A Gagliardi Materials Research Society Fall Meeting, Materials Research Society (MRS …, 2018 | | 2018 |
