Cited by 303Explainable Artificial Intelligence (XAI)Advanced Neural Network ApplicationsMachine Learning and Data Classification

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Improving performance of deep learning models with axiomatic attribution priors and expected gradients

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Erion, Gabriel, Janizek, Joseph D., Sturmfels, Pascal, Lundberg, Scott M., Lee, Su-In·2021·Nature Machine Intelligence

DOI 10.1038/s42256-021-00343-w

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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
ORCID: Erion, Gabriel
Janizek, Joseph D.
Lundberg, Scott M.
Lee, Su-In
Venue: Nature Machine Intelligence
Publisher: Springer Science and Business Media LLC
Published: 2021-05-31
Volume: 3
Issue: 7
Pages: 620-631
ISSN: 2522-5839 (ISSN-L)
DOI: 10.1038/s42256-021-00343-w
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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Improving performance of deep learning models with axiomatic attribution priors and expected gradients

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