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2020 – today
- 2024
[c3]Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien, Marina M.-C. Höhne:
Explainable AI in grassland monitoring: Enhancing model performance and domain adaptability. GIL 2024: 143-154- [c2]Anna Hedström, Leander Weber
, Sebastian Lapuschkin, Marina M.-C. Höhne:
A Fresh Look at Sanity Checks for Saliency Maps. xAI (1) 2024: 403-420 - [i8]Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne:
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test. CoRR abs/2401.06465 (2024) - [i7]Anna Hedström, Leander Weber, Sebastian Lapuschkin, Marina M.-C. Höhne:
A Fresh Look at Sanity Checks for Saliency Maps. CoRR abs/2405.02383 (2024) - [i6]Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov:
CoSy: Evaluating Textual Explanations of Neurons. CoRR abs/2405.20331 (2024) - 2023
- [j2]Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond. J. Mach. Learn. Res. 24: 34:1-34:11 (2023) - [j1]Anna Hedström, Philine Lou Bommer, Kristoffer Knutsen Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. Trans. Mach. Learn. Res. 2023 (2023) - [i5]Anna Hedström, Philine Lou Bommer, Kristoffer K. Wickstrøm, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus. CoRR abs/2302.07265 (2023) - [i4]Philine Lou Bommer, Marlene Kretschmer, Anna Hedström, Dilyara Bareeva, Marina M.-C. Höhne:
Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science. CoRR abs/2303.00652 (2023) - [i3]Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda, Cornelia Weltzien, Marina M.-C. Höhne:
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability. CoRR abs/2312.08408 (2023) - 2022
- [c1]Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne:
NoiseGrad - Enhancing Explanations by Introducing Stochasticity to Model Weights. AAAI 2022: 6132-6140 - [i2]Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne:
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations. CoRR abs/2202.06861 (2022) - 2021
- [i1]Kirill Bykov, Anna Hedström, Shinichi Nakajima, Marina M.-C. Höhne:
NoiseGrad: enhancing explanations by introducing stochasticity to model weights. CoRR abs/2106.10185 (2021)
Coauthor Index
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