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Elements: Portable machine learning models for experimental nuclear physics
https://doi.org/10.6084/m9.figshare.24274399
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Description
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We train portable ML models using self-supervised methods
on data from nuclear physics experiments that will then be released to the community.These models can be fine-tuned by end-users for a variety of downstream applications.
Michelle Kuchera
Raghuram Ramanujan
DOI registered
October 9, 2023
via DataCite
Poster published 2023 in
figshare Academic Research System
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