The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
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“Prior knowledge can be utilized to constrain explanations during training to improve model performance and produce high-quality attribution maps [37].”
From Transparency of deep neural networks for medical image analysis: A review of interpretability methods (2022) · cites this paper as [37]
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Phoeuk, M., Kim, S., & Kwon, M. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.113700
Prajoko, Rijati, N., Basuki, R. S., & Pujiono (2026) · International Seminar on Application for Technology of Information and Communication (iSemantic) · doi:10.1109/isemantic71495.2026.11742599
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Metadata
- Authors
- Erion, G., Janizek, J. D., Sturmfels, P., Lundberg, S. M., & Lee, S.-I.
- Affiliations
- University of Washington (US)
- Microsoft (United States) (US)
- Corresponding author
- Lee, Su-In
- Venue
- Nature Machine Intelligence
- Published
- 2021-05-31
- Volume
- 3
- Issue
- 7
- Pages
- 620-631
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3105856249
- Cited by
- 300
- References
- 54
- Citations by year
- 2026: 85 · 2025: 61 · 2024: 65 · 2023: 52 · 2022: 17 · 2021: 20
- FWCI
- 13.73
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (R35GM128638, F30HL151074-01, 5T32GM007266-46, R01AG061132, DGE-1256082)
- National Science Foundation (DBI-1552309, DBI-1759487)
- American Cancer Society (RSG-14-257-01-TBG)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- feature attribution, model performance improvement, completeness axiom, neural network interpretability
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