Individualisierte Tierhaltung

Aufsätze in referierten Fachzeitschriften [16 Ergebnisse]

• Bykov, K.; Höhne, M.; Creosteanu, A.; Muller, K.; Klauschen, F.; Nakajima, S.; Kloft, M. (2025): Explaining Bayesian Neural Networks. Transactions on Machine Learning Research. (09): p. 1-25. Online: https://openreview.net/pdf?id=ZxsR4t3wJd
• Bommer, P.; Kretschmer, M.; Spuler, F.; Bykov, K.; Höhne, M. (2025): Deep learning meets teleconnections: improving S2S predictions for European winter weather. Machine Learning: Earth. (1): p. 15002. Online: https://doi.org/10.1088/3049-4753/ade9c2
• Hedström, A.; Bommer, P.; Burns, T.; Lapuschkin, S.; Samek, W.; Höhne, M. (2025): Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions. Transactions on Machine Learning Research. : p. 1-48. Online: https://openreview.net/pdf?id=ukLxqA8zXj
• Babor, M.; Liu, S.; Arefi, A.; Olszewska-Widdrat, A.; Sturm, B.; Venus, J.; Höhne, M. (2025): Interpretable Domain Adaptation Enables Robust Lactic Acid Fermentation Monitoring from Waste. SSRN. : p. 1-20. Online: http://dx.doi.org/10.2139/ssrn.5012080
• Arefi, A.; Sturm, B.; Babor, M.; Horf, M.; Hoffmann, T.; Höhne, M.; Friedrich, K.; Schroedter, L.; Venus, J.; Olszewska-Widdrat, A. (2024): Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates. Heliyon. (19): p. 38791. Online: https://doi.org/10.1016/j.heliyon.2024.e38791
• Olszewska-Widdrat, A.; Babor, M.; Höhne, M.; Alexandri, M.; López Gómez, J.; Venus, J. (2024): A mathematical model-based evaluation of yeast extract’s effects on microbial growth and substrate consumption for lactic acid production by Bacillus coagulans. Process Biochemistry. (November): p. 304-315. Online: https://doi.org/10.1016/j.procbio.2024.07.017
• Bommer, P.; Kretschmer, M.; Hedström, A.; Bareeva, D.; Höhne, M. (2024): Finding the right XAI Method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science. Artificial Intelligence for the Earth Systems (AIES). (3): p. 1-26. Online: https://doi.org/10.1175/AIES-D-23-0074.1
• Hedström, A.; Weber, L.; Lapuschkin, S.; Höhne, M. (2024): Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. arXiv. : p. 1-19. Online: https://arxiv.org/abs/2401.06465
• Gautam, S.; Boubekki, A.; Höhne, M.; Kampffmeyer, M. (2023): Prototypical Self-Explainable Models Without Re-training. arXiv. : p. 1-25. Online: https://arxiv.org/abs/2312.07822
• Grinwald, D.; Bykov, K.; Nakajima, S.; Höhne, M. (2023): Visualizing the Diversity of Representations Learned by Bayesian Neural Networks. Transactions on Machine Learning Research. (11): p. 1-25. Online: https://openreview.net/pdf?id=ZSxvyWrX6k

Beiträge zu Sammelwerken [17 Ergebnisse]

• 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: Advances in Neural Information Processing Systems 39 (NeurIPS 2025). NeurIPS 2025. p. 1-29. Online: https://openreview.net/pdf?id=svywqosucP
• Rossi, G.; Altavilla, A.; Rossi Ribeiro, L.; Höhne, M.; Babor, M.; Schlüter, O. (2025): Effect of ultrasound processing on physico-chemical properties of edible insects: a preliminary investigation. In: Piofczyk, T.; Haupentha, K.(eds.): INSECTA 2025 International Conference - Book of Abstracts. INSECTA 2025. p. 39-39. Online: https://www.oehmi-analytik.de/images/INSECTA/INSECTA_BoA_2025.pdf
• 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
• Liu, S.; Babor, M.; Munyendo, L.; Hitzmann, B.; Sturm, B.; Höhne, M. (2024): Advancements in coffee authenticity: A spectroscopic feature compression approach using eXplainable AI and vision transformer. In: Katsoulas, N.(eds.): AgEng 2024 Proceedings Book. AgEng 2024 International Conference of EurAgEng. Hellenic Society of Agricultural Engineers, Athens, Greece, (978-618-82194-1-0), p. 436-442. Online: https://convin.gr/assets/files/misc/AgEng2024_Proceedings_ISBN.pdf
• Yu, X.; Franzen, J.; Samek, W.; Höhne, M.; Kainmueller, D. (2024): Model guidance via explanations turns image classifiers into segmentation models. In: Longo, L.; Lapuschkin, S.; Seifert, C.(eds.): Explainable Artificial Intelligence, Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part II. 2nd World Conference on eXplainable Artificial Intelligence (XAI-2024). Springer, Cham, Switzerland, (1865-0929/978-3-031-63796-4), p. 113-129. Online: https://doi.org/10.1007/978-3-031-63797-1_7
• Wickstrøm, K.; Höhne, M.; Hedström, A. (2024): From Flexibility to Manipulation. The Slippery Slope of XAI Evaluation. In: Explainable Computer Vision: Where are We and Where are We Going?. eXCV Workshop at ECCV 2024. p. 1-19. Online: https://excv-workshop.github.io/publication/from-flexibility-to-manipulation-the-slippery-slope-of-xai-evaluation/paper.pdf
• Kopf, L.; Bommer, P.; Hedström, A.; Lapushkin, S.; Höhne, M.; Bykov, K. (2024): CoSy: Evaluating Textual Explanations of Neurons. In: Next generation of AI Safety Workshop at ICML 2024. ICML Workshop Next Generation of AI Safety. p. 1-21. Online: https://arxiv.org/abs/2405.20331
• Kopf, L.; Bommer, P.; Hedström, A.; Lapushkin, S.; Höhne, M.; Bykov, K. (2024): CoSy: Evaluating Textual Explanations of Neurons. In: Mechanistic Interpretability Workshop at ICML 2024. ICML Workshop on Mechanistic Interpretability. p. 1-21. Online: https://arxiv.org/abs/2405.20331
• Kopf, L.; Bommer, P.; Hedström, A.; Lapushkin, S.; Höhne, M.; Bykov, K. (2024): CoSy: Evaluating Textual Explanations of Neurons. In: Advances in Neural Information Processing Systems 38 (NeurIPS 2024 Proceedings). The 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). p. 1-21. Online: https://arxiv.org/abs/2405.20331
• Hedström, A.; Weber, L.; Lapuschkin, S.; Höhne, M. (2024): A Fresh Look at Sanity Checks for Saliency Maps. In: Longo, L.; Lapuschkin, S.; Seifert, C.(eds.): Explainable Artificial Intelligence, Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part I. 2nd World Conference on eXplainable Artificial Intelligence (XAI-2024). Springer, Cham, Switzerland, (1865-0929/978-3-031-63786-5), p. 403-420. Online: https://doi.org/10.1007/978-3-031-63787-2_21

Vorträge und Poster [28 Ergebnisse]

• 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.
• Liu, S.; Babor, M.; Verduyn, C.; Vandenberghe, B.; Betoni Parodi, B.; Weltzien, C.; Höhne, M. (2025): LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping.
• Babor, M.; Arefi, A.; Sturm, B.; Höhne, M.; Zude-Sasse, M. (2025): From 3D Geometry and Spectral Intensity to Moisture Insight: LiDAR-Based Postharvest Quality Monitoring.
• Rossi, G.; Altavilla, A.; Rossi Ribeiro, L.; Höhne, M.; Babor, M.; Schlüter, O. (2025): Effect of ultrasound processing on physico-chemical properties of edible insects: a preliminary investigation.
• Liu, S.; Babor, M.; Verduyn, C.; Vandenberghe, B.; Betoni Parodi, B.; Weltzien, C.; Höhne, M. (2025): High-Resolution Leaf Tracking for Real-World Crop Phenotyping: Introducing CanolaTrack and LeafTrackNet.
• Höhne, M. (2025): How much can I trust you? Towards Understanding Neural Networks.
• Bommer, P.; Kretschmer, M.; Spurler, F.; Bykov, K.; Boehnke, P.; Höhne, M. (2025): Combining spatio-temporal neural networks with mechanistic interpretability to investigate teleconnections in S2S forecasts.
• Hedström, A.; Weber, L.; Lapuschkin, S.; Höhne, M. (2024): Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test.
• Bareeva, D.; Höhne, M.; Warnecke, A.; Pirch, L.; Muller, K.; Rieck, K.; Bykov, K. (2024): Manipulating Feature Visualizations with Gradient Slingshots.
• Kopf, L.; Bommer, P.; Hedström, A.; Lapuschkin, S.; Höhne, M.; Bykov, K. (2024): CoSy: Evaluating Textual Explanations of Neurons.