Marina M.-C. Höhne

49 papers A* 5Journal 35Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence. CoRR Georgii Mikriukov, Grégoire Montavon, Marina M.-C. Höhne
2025	J	`jnl`	Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework. CoRR Laura Kopf, Nils Feldhus, Kirill Bykov, Philine Lou Bommer, Anna Hedström, Marina M.-C. Höhne, Oliver Eberle
2025	J	`jnl`	Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather. CoRR Philine Lou Bommer, Marlene Kretschmer, Fiona R. Spuler, Kirill Bykov, Marina M.-C. Höhne
2025	A*	`conf`	Evaluate with the Inverse: Efficient Approximation of Latent Explanation Quality Distribution. AAAI Carlos Eiras-Franco, Anna Hedström, Marina M.-C. Höhne
2025	J	`jnl`	Evaluate with the Inverse: Efficient Approximation of Latent Explanation Quality Distribution. CoRR Carlos Eiras-Franco, Anna Hedström, Marina M.-C. Höhne
2025	J	`jnl`	Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions. Trans. Mach. Learn. Res. Anna Hedström, Philine Lou Bommer, Thomas F. Burns, Sebastian Lapuschkin, Wojciech Samek, Marina M.-C. Höhne
2025	J	`jnl`	Explaining Bayesian Neural Networks. Trans. Mach. Learn. Res. Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft
2025	J	`jnl`	LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping. CoRR Shanghua Liu, Majharulislam Babor, Christoph Verduyn, Breght Vandenberghe, Bruno Betoni Parodi, Cornelia Weltzien, Marina M.-C. Höhne
2024	—	`conf`	A Fresh Look at Sanity Checks for Saliency Maps. xAI (1) Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne
2024	J	`jnl`	A Fresh Look at Sanity Checks for Saliency Maps. CoRR Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne
2024	A*	`conf`	CoSy: Evaluating Textual Explanations of Neurons. NeurIPS Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov
2024	J	`jnl`	CoSy: Evaluating Textual Explanations of Neurons. CoRR Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov
2024	—	`conf`	Deep Learning-based UAV-assisted grassland monitoring to facilitate Eco-scheme 5 realization. GIL Deepak Hanike Basavegowda, Marina M.-C. Höhne, Cornelia Weltzien
2024	—	`conf`	Explainable AI in grassland monitoring: Enhancing model performance and domain adaptability. GIL Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien, Marina M.-C. Höhne
2024	—	`conf`	From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation. ECCV Workshops (21) Kristoffer Wickstrøm, Marina M.-C. Höhne, Anna Hedström
2024	J	`jnl`	Manipulating Feature Visualizations with Gradient Slingshots. CoRR Dilyara Bareeva, Marina M.-C. Höhne, Alexander Warnecke, Lukas Pirch, Klaus-Robert Müller, Konrad Rieck, Kirill Bykov
2024	—	`conf`	Model Guidance via Explanations Turns Image Classifiers into Segmentation Models. xAI (2) Xiaoyan Yu, Jannik Franzen, Wojciech Samek, Marina M.-C. Höhne, Dagmar Kainmueller
2024	J	`jnl`	Model Guidance via Explanations Turns Image Classifiers into Segmentation Models. CoRR Xiaoyan Yu, Jannik Franzen, Wojciech Samek, Marina M.-C. Höhne, Dagmar Kainmueller
2024	J	`jnl`	Prototypical Self-Explainable Models Without Re-training. Trans. Mach. Learn. Res. Srishti Gautam, Ahcene Boubekki, Marina M.-C. Höhne, Michael Kampffmeyer
2024	J	`jnl`	Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. CoRR Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne
2023	J	`jnl`	DORA: Exploring Outlier Representations in Deep Neural Networks. Trans. Mach. Learn. Res. Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne
2023	J	`jnl`	Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability. CoRR Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien, Marina M.-C. Höhne
2023	—	`conf`	Finding Spurious Correlations with Function-Semantic Contrast Analysis. xAI (2) Kirill Bykov, Laura Kopf, Marina M.-C. Höhne
2023	J	`jnl`	Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science. CoRR Philine Lou Bommer, Marlene Kretschmer, Anna Hedström, Dilyara Bareeva, Marina M.-C. Höhne
2023	J	`jnl`	Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors. CoRR Pia Hanfeld, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig
2023	—	`conf`	Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. MRS Pia Hanfeld, Khaled Wahba, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig
2023	J	`jnl`	Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches. CoRR Pia Hanfeld, Khaled Wahba, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig
2023	A*	`conf`	Labeling Neural Representations with Inverse Recognition. NeurIPS Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne
2023	J	`jnl`	Labeling Neural Representations with Inverse Recognition. CoRR Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne
2023	—	`conf`	Mark My Words: Dangers of Watermarked Images in ImageNet. ECAI Workshops (1) Kirill Bykov, Klaus-Robert Müller, Marina M.-C. Höhne
2023	J	`jnl`	Mark My Words: Dangers of Watermarked Images in ImageNet. CoRR Kirill Bykov, Klaus-Robert Müller, Marina M.-C. Höhne
2023	J	`jnl`	Prototypical Self-Explainable Models Without Re-training. CoRR Srishti Gautam, Ahcene Boubekki, Marina M.-C. Höhne, Michael C. Kampffmeyer
2023	J	`jnl`	Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. J. Mach. Learn. Res. Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne
2023	J	`jnl`	The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. CoRR Anna Hedström, Philine Lou Bommer, Kristoffer K. Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne
2023	J	`jnl`	The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. Trans. Mach. Learn. Res. Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne
2023	J	`jnl`	Visualizing the Diversity of Representations Learned by Bayesian Neural Networks. Trans. Mach. Learn. Res. Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne
2022	J	`jnl`	DORA: Exploring outlier representations in Deep Neural Networks. CoRR Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne
2022	J	`jnl`	Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation. CoRR Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer
2022	—	`conf`	Demonstrating the Risk of Imbalanced Datasets in Chest X-Ray Image-Based Diagnostics by Prototypical Relevance Propagation. ISBI Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer
2022	A*	`conf`	NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights. AAAI Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne
2022	A*	`conf`	ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. NeurIPS Srishti Gautam, Ahcène Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina M.-C. Höhne, Michael Kampffmeyer
2022	J	`jnl`	ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. CoRR Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina M.-C. Höhne, Michael Kampffmeyer
2022	J	`jnl`	Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations. CoRR Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne
2022	J	`jnl`	Visualizing the diversity of representations learned by Bayesian neural networks. CoRR Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne
2021	J	`jnl`	Explaining Bayesian Neural Networks. CoRR Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft
2021	J	`jnl`	NoiseGrad: enhancing explanations by introducing stochasticity to model weights. CoRR Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne
2021	J	`jnl`	Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout. CoRR Yamen Ali, Aiham Taleb, Marina M.-C. Höhne, Christoph Lippert
2021	J	`jnl`	This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. CoRR Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer
2020	J	`jnl`	How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. CoRR Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft