Our team

Articles in peer reviewed journals [15 Results]

• 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. (2024): Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation. Bioresource Technology. : p. 1-23. Online: Preprint: 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
• Bareeva, D.; Höhne, M.; Warnecke, A.; Pirch, L.; Müller, K.; Rieck, K.; Bykov, K. (2024): Manipulating Feature Visualizations with Gradient Slingshots. arXiv. : p. 1-19. Online: https://doi.org/10.48550/arXiv.2401.06122
• 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
• Hanfeld, P.; Wahba, K.; Höhne, M.; Bussmann, M.; Hönig, W. (2023): Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. arXiv. : p. 1-7. Online: https://doi.org/10.48550/arXiv.2308.00344

Bookchapters and proceedings [15 Results]

• 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
• Bareeva, D.; Höhne, M.; Warnecke, A.; Pirch, L.; Müller, K.; Rieck, K.; Bykov, K. (2024): Manipulating Feature Visualizations with Gradient Slingshots. In: Mechanistic Interpretability Workshop at ICML 2024. ICML 2024 - International Conference on Machine Learning. p. 1-18. Online: https://doi.org/10.48550/arXiv.2401.06122
• 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
• Liu, S.; Hedström, A.; Hanike Basavegowda, D.; Weltzien, C.; Höhne, M. (2024): Explainable AI in grassland monitoring: Enhancing model performance and domain adaptability. In: Hoffmann, C.; Stein, A.; Gallmann, E.; Dörr, J.; Krupitzer, C.; Floto, H.(eds.): Informatik in der Land-, Forst- und Ernährungswirtschaft. Focus: Biodiversität fördern durch digitale Landwirtschaft: Welchen Beitrag leisten KI und Co?. 44. GIL-Jahrestagung - Biodiversität fördern durch digitale Landwirtschaft: Welchen Beitrag leisten KI und Co?. Gesellschaft für Informatik (GI), Bonn, (1617-5468/978-3-88579-738-8), p. 143-154. Online: https://gil-net.de/wp-content/uploads/2024/02/GI_Proceedings_344-3.f-1.pdf
• Hanike Basavegowda, D.; Höhne, M.; Weltzien, C. (2024): Deep Learning-based UAV-assisted grassland monitoring to facilitate Eco-scheme 5 realization. In: Hoffmann, C.; Stein, A.; Gallmann, E.; Dörr, J.; Krupitzer, C.; Floto, H.(eds.): Informatik in der Land-, Forst- und Ernährungswirtschaft. Focus: Biodiversität fördern durch digitale Landwirtschaft: Welchen Beitrag leisten KI und Co?. 44. GIL-Jahrestagung - Biodiversität fördern durch digitale Landwirtschaft: Welchen Beitrag leisten KI und Co?. Gesellschaft für Informatik (GI), Bonn, (1617-5468/978-3-88579-738-8), p. 197-202. Online: https://gil-net.de/wp-content/uploads/2024/02/GI_Proceedings_344-3.f-1.pdf

Orals and posters [22 Results]

• 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.
• 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.
• Liu, S.; Hedström, A.; Hanike Basavegowda, D.; Weltzien, C.; Höhne, M. (2024): Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability.
• Hanike Basavegowda, D.; Höhne, M.; Weltzien, C. (2024): Deep Learning-based UAV-assisted grassland monitoring to facilitate Eco-scheme 5 realization.
• Hedström, A.; Weber, L.; Lapuschkin, S.; Höhne, M. (2023): Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test.
• Hanfeld, P.; Wahba, K.; Höhne, M.; Bussmann, M.; Hoenig, W. (2023): Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches.
• Bykov, K.; Kopf, L.; Nakajima, S.; Kloft, M.; Höhne, M. (2023): Labeling Neural Representations with Inverse Recognition.