Duarte, Javier https://orcid.org/0000-0002-5076-7096
Li, Haoyang https://orcid.org/0000-0003-2599-4948
Roy, Avik https://orcid.org/0000-0002-0116-1012
Zhu, Ruike
Huerta, E A https://orcid.org/0000-0002-9682-3604
Diaz, Daniel https://orcid.org/0000-0001-6834-1176
Harris, Philip https://orcid.org/0000-0001-8189-3741
Kansal, Raghav https://orcid.org/0000-0003-2445-1060
Katz, Daniel S https://orcid.org/0000-0001-5934-7525
Kavoori, Ishaan H
Kindratenko, Volodymyr V https://orcid.org/0000-0002-9336-4756
Mokhtar, Farouk https://orcid.org/0000-0003-2533-3402
Neubauer, Mark S https://orcid.org/0000-0001-8434-9274
Eon Park, Sang https://orcid.org/0000-0003-3225-0007
Quinnan, Melissa https://orcid.org/0000-0003-2902-5597
Rusack, Roger https://orcid.org/0000-0002-7633-749X
Zhao, Zhizhen
Funding for this research was provided by:
Argonne National Laboratory (DE-AC02-06CH11357)
Office of Science (DE-SC0021225)
National Science Foundation (1725729)
Article Title: FAIR AI models in high energy physics
Journal Title: Machine Learning: Science and Technology
Article Type: paper
Copyright Information: © 2023 The Author(s). Published by IOP Publishing Ltd
Publication dates
Date Received: 2022-12-21
Date Accepted: 2023-12-06
Online publication date: 2023-12-29