Beiträge zu Sammelwerken
- Hoffmann, T. (2026): Halmgutkonservierung. In: Frerichs, L.(eds.): Jahrbuch Agrartechnik 2025. . Braunschweig, p. 1-9. 1.0
- Suhr, D.; Tessaro, V.; Koch, K.; Jalali, A.; Dworak, V.; Schröder, S.; Schütte, T.; Weltzien, C. (2025): A Feedback Control Architecture for Low-Cost Lasers and Improved Operational Efficiency in Laser Weed Control. In: 82nd International Conference on Agricultural Engineering Land.Technik AgEng 2025. 82nd International Conference on Agricultural Engineering Land.Technik AgEng 2025. VDI Verlag, Düsseldorf, (0083-5560/978-3-18-092465-6), p. 445-450. 1.0
- Höhne, M. (2025): Explaining 4.0: Künstliche Intelligenz - Transparenz und Effizienz. In: Forschungsberichte Pflichtabgabe (BMFTR, BMWE). . Hannover, p. 1-18. Online: https://doi.org/10.34657/19380 1.0
- Kopf, L.; Feldhus, N.; Bykov, K.; Bommer, P.; Hedström, A.; Höhne, M.; Eberle, O. (2025): Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework. In: Mechanistic Interpretability Workshop (NeurIPS 2025). Mechanistic Interpretability Workshop, NeurIPS 2025. p. 1-29. Online: https://openreview.net/forum?id=svywqosucP 1.0
- Bareeva, D.; Höhne, M.; Warnecke, A.; Pirch, L.; Müller, K.; Rieck, K.; Lapuschkin, S.; Bykov, K. (2025): Manipulating Feature Visualizations with Gradient Slingshots. In: Advances in Neural Information Processing Systems 39 (NeurIPS 2025). NeurIPS 2025. p. 1-38. Online: https://openreview.net/pdf/c7e275543fb73c523928b41f35e776058b9f5b26.pdf 1.0
- Matavel, C.; Meyer-Aurich, A. (2025): Data-driven nitrogen management: Leveraging historical data and machine learning for economic optima. Lecture Notes in Informatics (LNI). In: Informatik in der Land-, Forst- und Ernährungswirtschaft. 45. GIL-Jahrestagung - Informatik in der Land-, Forst- und Ernährungswirtschaft. Bonn, (1617-5468 (Online)), p. 323-328. Online: https://gil-net.de/Publikationen/GIL25_Proceedings_final_01.pdf 1.0
- Fumagalli, F.; Muschalik, M.; Hüllermeier, E.; Hammer, B.; Herbinger, J. (2025): Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory. In: Li, Y.; Mandt, S.; Khan, E.(eds.): Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS). AISTATS 2025. PMLR, (2640-3498), p. 1-42. Online: https://proceedings.mlr.press/v258/fumagalli25a.html 1.0
- (2025): Evaluate with the Inverse: Efficient Approximation of Latent Explanation Quality Distribution. In: Proceedings of the AAAI Conference on Artificial Intelligence. 39th AAAI Conference on Artificial Intelligence (AAAI-25). Washington, DC 20004, USA, (2374-3468/1-57735-897-X), p. 27258-27267. Online: https://doi.org/10.1609/aaai.v39i26.34935 1.0
- Genzel, M.; Putzky, P.; Zhao, P.; Schulze, S.; Mollenhauer, M.; Seidel, R.; Dietzel, S.; Wollmann, T. (2025): Compressing Large Language Models to Any Size Without Re-Computation. In: Proceedings of the Workshop on Efficient Systems for Foundation Models (ES-FoMo) at the 42 nd International Conference on Machine Learning (2025), Vancouver, Canada. Efficient Systems for Foundation Models Workshop at the International Conference on Machine Learning (ICML) 2025. p. 1-23. Online: https://icml.cc/virtual/2025/51788 1.0
- Zude-Sasse, M. (2025): Fusion von Sensordaten im Gartenbau. In: Frerichs, L.(eds.): Jahrbuch Agrartechnik 2024. . Braunschweig, p. 1-6. Online: https://leopard.tu-braunschweig.de/receive/dbbs_mods_00078431 1.0