| Bayesian optimisation for fast and safe parameter tuning of swissfel J Kirschner, M Nonnenmacher, M Mutný, A Krause, N Hiller, R Ischebeck, ... FEL2019, Proceedings of the 39th International Free-Electron Laser …, 2019 | 24 | 2019 |
| Utilizing expert features for contrastive learning of time-series representations MT Nonnenmacher, L Oldenburg, I Steinwart, D Reeb International Conference on Machine Learning, 16969-16989, 2022 | 18 | 2022 |
| Sosp: Efficiently capturing global correlations by second-order structured pruning M Nonnenmacher, T Pfeil, I Steinwart, D Reeb arXiv preprint arXiv:2110.11395, 2021 | 17 | 2021 |
| Which Minimizer Does My Neural Network Converge To? M Nonnenmacher, D Reeb, I Steinwart Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021 | 12 | 2021 |
| Efficient second order pruning of computer-implemented neural networks M Nonnenmacher, D Reeb, T Pfeil US Patent App. 17/812,207, 2023 | | 2023 |
| Generation of simplified computer-implemented neural networks D Reeb, M Nonnenmacher US Patent App. 17/450,773, 2022 | | 2022 |
| SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning M Nonnenmacher, T Pfeil, I Steinwart, D Reeb International Conference on Learning Representations, 2021 | | 2021 |
| Wide Neural Networks are Interpolating Kernel Methods: Impact of Initialization on Generalization M Nonnenmacher, D Reeb, I Steinwart | | 2019 |
