Feature Attribution is a computational technique in Explainable Artificial Intelligence in Education that identifies and quantifies the contribution of individual input features to a model's predictions, thereby revealing their relative importance in shaping educational outcomes.
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455
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185
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2017–2026
125
Publications
134
Axiomatic Attribution for Deep Networks
Mukund Sundararajan, Ankur Taly, Qiqi Yan · arXiv (Cornell University) · 2017 · 2.6K citations · Full text
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455
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Institutions
185
University of Washington
Seattle, United States
10 h-index
29 publications
6.1K citations
Google (United States)
Mountain View, United States
8 h-index
28 publications
12.9K citations
ETH Zurich
Zurich, Switzerland
4 h-index
13 publications
460 citations
University of California, Los Angeles
Los Angeles, United States
4 h-index
8 publications
244 citations
National University of Singapore
Singapore, Singapore
4 h-index
9 publications
212 citations
University of Science and Technology of China
Hefei, China
4 h-index
11 publications
345 citations
Chinese Academy of Sciences
Beijing, China
3 h-index
5 publications
241 citations
Colorado State University
Fort Collins, United States
3 h-index
6 publications
174 citations
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- arXiv (Cornell University)
Repository · 41 publications · 6K citations
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 6 publications · 282 citations
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- Findings of the Association for Computational Linguistics: ACL 2022
Conference · 2 publications · 48 citations
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- Nature Communications
2 publications · 324 citations
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- Bioinformatics
2 publications · 97 citations
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