Material and energetic use of biomass

Aufsätze in referierten Fachzeitschriften [14 Results]

• 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. 1-13. 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). : p. 1-55. 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
• 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
• Hedström, A.; Weber, L.; Lapuschkin, S.; Höhne, M. (2023): Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. arXiv. : p. 1-19. Online: https://arxiv.org/abs/2401.06465
• Bykov, K.; Kopf, L.; Nakajima, S.; Kloft, M.; Höhne, M. (2023): Labeling Neural Representations with Inverse Recognition. arXiv. : p. 1-24. Online: https://doi.org/10.48550/arXiv.2311.13594
• 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
• Hanfeld, P.; Höhne, M.; Bussmann, M.; Hönig, W. (2023): Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors. arXiv. : p. 1-6. Online: https://doi.org/10.48550/arXiv.2305.12859

Beiträge zu Sammelwerken [12 Results]

• Bommer, P.; Kretschmer, M.; Boehnke, P.; Höhne, M. (2024): Using spatio-temporal neural networks to investigating teleconnections and enhance S2S forecasts of european extreme weather. In: EGU General Assembly 2024 Proceedings. EGU General Assembly 2024. p. 15174-0. Online: https://doi.org/10.5194/egusphere-egu24-15174
• 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: . 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: . ICML Workshop on Mechanistic Interpretability. 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: . 2nd World Conference on eXplainable Artificial Intelligence (XAI-2024). Springer, Heidelberg, p. 1-26. Online: https://doi.org/10.48550/arXiv.2405.02383
• 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
• Bykov, K.; Müller, K.; Höhne, M. (2024): Mark My Words: Dangers of Watermarked Images in ImageNet. In: Nowaczyk, S.; et al.(eds.): Artificial Intelligence. ECAI 2023 International Workshops. Proceedings, Part I. ECAI 2023 XI-ML Workshops. Springer, Cham, Switzerland, (1865-0929 / 978-3-031-50396-2), p. 426-434. Online: https://doi.org/10.1007/978-3-031-50396-2_24
• Bykov, K.; Müller, K.; Höhne, M. (2023): Mark My Words: Dangers of Watermarked Images in ImageNet. In: ICLR2023 workshop on Pitfalls of limited data and computation for Trustworthy ML. ICLR 2023. p. 1-10. Online: https://openreview.net/forum?id=0stsgHlCxS
• Hedström, A.; Weber, L.; Lapuschkin, S.; Höhne, M. (2023): Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. In: XAI in Action: Past, Present, and Future Applications. NeurIPS 2023. Neural Information Processing Systems, San Diego, p. 1-19. Online: https://openreview.net/forum?id=vVpefYmnsG

Vorträge und Poster [21 Results]

• 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.
• Bykov, K.; Müller, K.; Höhne, M. (2023): Mark My Words: Dangers of Watermarked Images in ImageNet.