Towards novel insights in lattice field theory with explainable machine learning S Blücher, L Kades, JM Pawlowski, N Strodthoff, JM Urban Physical Review D 101 (9), 094507, 2020 | 48 | 2020 |
Reweighting Lefschetz Thimbles S Bluecher, JM Pawlowski, M Scherzer, M Schlosser, IO Stamatescu, ... SciPost Physics 5 (5), 044, 2018 | 26 | 2018 |
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees J Vielhaben, S Blücher, N Strodthoff Transactions on Machine Learning Research, 2023 | 18 | 2023 |
PredDiff: Explanations and Interactions from Conditional Expectations S Blücher, J Vielhaben, N Strodthoff Artificial Intelligence 312, 103774, 2022 | 11 | 2022 |
Sparse subspace clustering for concept discovery (SSCCD) J Vielhaben, S Blücher, N Strodthoff arXiv preprint arXiv:2203.06043, 2022 | 5 | 2022 |
Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks S Blücher, J Vielhaben, N Strodthoff arXiv preprint arXiv:2401.06654, 2024 | 2 | 2024 |
Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence S Blücher, KR Müller, S Chmiela Journal of Chemical Theory and Computation 19 (14), 4619-4630, 2023 | 2 | 2023 |