基本信息
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个人简介
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.
研究兴趣
论文共 94 篇作者统计合作学者相似作者
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PATHOLOGIEno. 2 (2024): 133-139
CoRR (2024)
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arxiv(2024)
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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
CoRR (2024)
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CoRR (2024)
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作者统计
#Papers: 95
#Citation: 18267
H-Index: 37
G-Index: 67
Sociability: 5
Diversity: 2
Activity: 107
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