@article{3606bfd432204c7d80874e8f085189cd,
title = "Equipping machine learning with uncertainty quantification to update probabilistic risk assessment of nuclear power plants using nrc licensee event reports",
author = "Jaemin Yang and Pegah Farshadmanesh and Tatsuya Sakurahara and Justin Pence and Seyed Reihani and Catherine Blake and Zalira Mohaghegh",
note = "Funding Information: This material is based on work supported by the National Science Foundation (NSF) under Grant No. 15.15167. Any opinions, findings, and conclusions or recommendations expressed in this material arc those of the author(s) and do not necessarily reflect the views of die National Science Foundation. The authors would like to thank all members of SoTeRiA (http://soteria.npre.Illinois.eduy) for their feedback.; 2020 Transactions of the American Nuclear Society Winter Meeting, ANS 2020 ; Conference date: 16-11-2020 Through 20-11-2020",
year = "2020",
doi = "10.13182/T123-33447",
language = "English (US)",
volume = "123",
pages = "280--283",
journal = "Transactions of the American Nuclear Society",
issn = "0003-018X",
publisher = "American Nuclear Society",
number = "1",
}