Physics Community Needs, Tools, and Resources for Machine Learning
Machine Learning
2022-03-31 v1 General Relativity and Quantum Cosmology
High Energy Physics - Experiment
Instrumentation and Detectors
Abstract
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.
Cite
@article{arxiv.2203.16255,
title = {Physics Community Needs, Tools, and Resources for Machine Learning},
author = {Philip Harris and Erik Katsavounidis and William Patrick McCormack and Dylan Rankin and Yongbin Feng and Abhijith Gandrakota and Christian Herwig and Burt Holzman and Kevin Pedro and Nhan Tran and Tingjun Yang and Jennifer Ngadiuba and Michael Coughlin and Scott Hauck and Shih-Chieh Hsu and Elham E Khoda and Deming Chen and Mark Neubauer and Javier Duarte and Georgia Karagiorgi and Mia Liu},
journal= {arXiv preprint arXiv:2203.16255},
year = {2022}
}
Comments
Contribution to Snowmass 2021, 33 pages, 5 figures