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Bio
His current research focuses on methods of explainable AI (XAI) for deep neural networks and unsupervised learning, and on closing the gap between existing XAI methods and practical desiderata. This includes using XAI to build more trustworthy machine learning models and using XAI to extract actionable insights from complex datasets. Jointly with his colleagues, he contributed to Layer-Wise Relevance Propagation (LRP), an efficient method for explaining the predictions of large deep neural networks. He and his co-authors also contributed to the “Neuralization-Propagation” framework which rewrites popular unsupervised learning models as functionally equivalent neural networks for explainability purposes, and higher-order extensions of LRP (BiLRP and GNN-LRP) which enable the identification of joint features contributions in models with product structures.
Research Interests
Papers共 97 篇Author StatisticsCo-AuthorSimilar Experts
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Frederick Klauschen, Jonas Dippel,Philipp Keyl,Philipp Jurmeister,Michael Bockmayr,Andreas Mock, Oliver Buchstab,Maximilian Alber,Lukas Ruff,Gregoire Montavon,Klaus-Robert Mueller
PATHOLOGIEno. 2 (2024): 133-139
CoRR (2024)
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Frederick Klauschen, Jonas Dippel,Philipp Keyl,Philipp Jurmeister,Michael Bockmayr,Andreas Mock, Oliver Buchstab,Maximilian Alber,Lukas Ruff,Gregoire Montavon,Klaus-Robert Mueller
ANNUAL REVIEW OF PATHOLOGY-MECHANISMS OF DISEASEno. 1 (2024): 541-570
IEEE transactions on pattern analysis and machine intelligenceno. 99 (2024): 1-18
PATTERN RECOGNITION (2024): 110309
arxiv(2024)
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Frederick Klauschen, Jonas Dippel,Philipp Keyl,Philipp Jurmeister,Michael Bockmayr,Andreas Mock, Oliver Buchstab,Maximilian Alber,Lukas Ruff,Grégoire Montavon,Klaus-Robert Müller
Forumno. 2 (2024): 1-7
CoRR (2024)
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INFORMATION FUSION (2024): 102094-102094
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