Prof. Dr. Marina Höhne
Articles in peer reviewed journals [16 Results]
- 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.; Venus, J.; Sturm, B.; Höhne, M. (2025): Interpretable Domain Adaptation Enables Robust Lactic Acid Fermentation Monitoring from Waste. Results in Engineering. (March 2026): p. 108477. Online: https://doi.org/10.1016/j.rineng.2025.108477
- 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