Marina M.-C. Höhne

According to our database, Marina M.-C. Höhne authored at least 28 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2024

Prototypical Self-Explainable Models Without Re-training.

Srishti Gautam, Ahcene Boubekki, Marina M.-C. Höhne, Michael Kampffmeyer

Trans. Mach. Learn. Res., 2024

CoSy: Evaluating Textual Explanations of Neurons.

Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov

CoRR, 2024

Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test.

Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne

CoRR, 2024

Manipulating Feature Visualizations with Gradient Slingshots.

Dilyara Bareeva, Marina M.-C. Höhne, Alexander Warnecke, Lukas Pirch, Klaus-Robert Müller, Konrad Rieck, Kirill Bykov

CoRR, 2024

Model Guidance via Explanations Turns Image Classifiers into Segmentation Models.

Xiaoyan Yu, Jannik Franzen, Wojciech Samek, Marina M.-C. Höhne, Dagmar Kainmueller

Proceedings of the Explainable Artificial Intelligence, 2024

A Fresh Look at Sanity Checks for Saliency Maps.

Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne

Proceedings of the Explainable Artificial Intelligence, 2024

Explainable AI in grassland monitoring: Enhancing model performance and domain adaptability.

Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien, Marina M.-C. Höhne

Proceedings of the 44. GIL-Jahrestagung, Informatik in der Land-, Forst- und Ernährungswirtschaft, 2024

Deep Learning-based UAV-assisted grassland monitoring to facilitate Eco-scheme 5 realization.

Deepak Hanike Basavegowda, Marina M.-C. Höhne, Cornelia Weltzien

Proceedings of the 44. GIL-Jahrestagung, Informatik in der Land-, Forst- und Ernährungswirtschaft, 2024

2023

<i>This</i> looks <i>More</i> Like <i>that</i>: Enhancing Self-Explaining Models by Prototypical Relevance Propagation.

Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer

Pattern Recognit., April, 2023

The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus.

Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne

Trans. Mach. Learn. Res., 2023

Visualizing the Diversity of Representations Learned by Bayesian Neural Networks.

Dennis Grinwald, Kirill Bykov, Shinichi Nakajima, Marina M.-C. Höhne

Trans. Mach. Learn. Res., 2023

DORA: Exploring Outlier Representations in Deep Neural Networks.

Kirill Bykov, Mayukh Deb, Dennis Grinwald, Klaus-Robert Müller, Marina M.-C. Höhne

Trans. Mach. Learn. Res., 2023

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond.

Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne

J. Mach. Learn. Res., 2023

Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors.

Pia Hanfeld, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig

CoRR, 2023

Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science.

Philine Lou Bommer, Marlene Kretschmer, Anna Hedström, Dilyara Bareeva, Marina M.-C. Höhne

CoRR, 2023

Finding Spurious Correlations with Function-Semantic Contrast Analysis.

Kirill Bykov, Laura Kopf, Marina M.-C. Höhne

Proceedings of the Explainable Artificial Intelligence, 2023

Labeling Neural Representations with Inverse Recognition.

Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches.

Pia Hanfeld, Khaled Wahba, Marina M.-C. Höhne, Michael Bussmann, Wolfgang Hönig

Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems, 2023

Mark My Words: Dangers of Watermarked Images in ImageNet.

Kirill Bykov, Klaus-Robert Müller, Marina M.-C. Höhne

Proceedings of the Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30, 2023

2022

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations.

Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne

CoRR, 2022

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model.

Srishti Gautam, Ahcène Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina M.-C. Höhne, Michael Kampffmeyer

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Demonstrating the Risk of Imbalanced Datasets in Chest X-Ray Image-Based Diagnostics by Prototypical Relevance Propagation.

Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer

Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022

NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights.

Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Nachvollziehbare Künstliche Intelligenz: Methoden, Chancen und Risiken.

Marina Marie-Claire Höhne

Datenschutz und Datensicherheit, 2021

Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout.

Yamen Ali, Aiham Taleb, Marina M.-C. Höhne, Christoph Lippert

CoRR, 2021

This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation.

Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer

CoRR, 2021

Explaining Bayesian Neural Networks.

Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft

CoRR, 2021

2020

How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks.

Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft

CoRR, 2020