| Explainability and interpretability in electric load forecasting using machine learning techniques–A review L Baur, K Ditschuneit, M Schambach, C Kaymakci, T Wollmann, A Sauer Energy and AI, 100358, 2024 | 21 | 2024 |
| Curve your enthusiasm: Concurvity regularization in differentiable generalized additive models J Siems, K Ditschuneit, W Ripken, A Lindborg, M Schambach, J Otterbach, ... Advances in Neural Information Processing Systems 36, 19029-19057, 2023 | 7 | 2023 |
| Auto-compressing subset pruning for semantic image segmentation K Ditschuneit, JS Otterbach DAGM German Conference on Pattern Recognition, 20-35, 2022 | 7 | 2022 |
| NAM-CAM: Neural-Additive Models for Semi-analytic Descriptions of CAM Simulations K Ditschuneit, A Frenk, M Frings, V Rudel, S Dietzel, JS Otterbach International Conference on Flexible Automation and Intelligent …, 2023 | 1 | 2023 |
| Pattern Recognition: 44th DAGM German Conference, DAGM GCPR 2022, Konstanz, Germany, September 27–30, 2022, Proceedings B Andres, F Bernard, D Cremers, S Frintrop, B Goldlücke, I Ihrke Springer Nature, 2022 | 1 | 2022 |
| Self-Distilled Representation Learning for Time Series F Pieper, K Ditschuneit, M Genzel, A Lindt, J Otterbach arXiv preprint arXiv:2311.11335, 2023 | | 2023 |
