I would like to bring to your attention that the following four published HEP ML papers will be a perfect fit for the living review.
Regression -- Lattice Gauge Theory
@article{Yoon:2018krb,
author = "Yoon, Boram and Bhattacharya, Tanmoy and Gupta, Rajan",
title = "{Machine Learning Estimators for Lattice QCD Observables}",
eprint = "1807.05971",
archivePrefix = "arXiv",
primaryClass = "hep-lat",
reportNumber = "LA-UR-18-26411",
doi = "10.1103/PhysRevD.100.014504",
journal = "Phys. Rev. D",
volume = "100",
number = "1",
pages = "014504",
year = "2019"
}
@article{Zhang:2019qiq,
author = "Zhang, Rui and Fan, Zhouyou and Li, Ruizi and Lin, Huey-Wen and Yoon, Boram",
title = "{Machine-learning prediction for quasiparton distribution function matrix elements}",
eprint = "1909.10990",
archivePrefix = "arXiv",
primaryClass = "hep-lat",
reportNumber = "MSUHEP-19-021",
doi = "10.1103/PhysRevD.101.034516",
journal = "Phys. Rev. D",
volume = "101",
number = "3",
pages = "034516",
year = "2020"
}
@article{Nguyen:2019gpo,
author = "Nguyen, Nga T. T. and Kenyon, Garrett T. and Yoon, Boram",
title = "{A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer}",
eprint = "1911.06267",
archivePrefix = "arXiv",
primaryClass = "quant-ph",
reportNumber = "LA-UR-19-31717",
doi = "10.1038/s41598-020-67769-x",
journal = "Sci. Rep.",
volume = "10",
number = "1",
pages = "10915",
year = "2020"
}
Generative models / density estimation -- Phase space generation
@article{Yoon:2020zmb,
author = "Yoon, Boram",
title = "{A machine learning approach for efficient multi-dimensional integration}",
eprint = "2009.06697",
archivePrefix = "arXiv",
primaryClass = "physics.comp-ph",
reportNumber = "LA-UR-20-27161",
doi = "10.1038/s41598-021-98392-z",
journal = "Sci. Rep.",
volume = "11",
pages = "18965",
year = "2021"
}
I would like to bring to your attention that the following four published HEP ML papers will be a perfect fit for the living review.
Regression -- Lattice Gauge Theory @article{Yoon:2018krb, author = "Yoon, Boram and Bhattacharya, Tanmoy and Gupta, Rajan", title = "{Machine Learning Estimators for Lattice QCD Observables}", eprint = "1807.05971", archivePrefix = "arXiv", primaryClass = "hep-lat", reportNumber = "LA-UR-18-26411", doi = "10.1103/PhysRevD.100.014504", journal = "Phys. Rev. D", volume = "100", number = "1", pages = "014504", year = "2019" }
@article{Zhang:2019qiq, author = "Zhang, Rui and Fan, Zhouyou and Li, Ruizi and Lin, Huey-Wen and Yoon, Boram", title = "{Machine-learning prediction for quasiparton distribution function matrix elements}", eprint = "1909.10990", archivePrefix = "arXiv", primaryClass = "hep-lat", reportNumber = "MSUHEP-19-021", doi = "10.1103/PhysRevD.101.034516", journal = "Phys. Rev. D", volume = "101", number = "3", pages = "034516", year = "2020" }
@article{Nguyen:2019gpo, author = "Nguyen, Nga T. T. and Kenyon, Garrett T. and Yoon, Boram", title = "{A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer}", eprint = "1911.06267", archivePrefix = "arXiv", primaryClass = "quant-ph", reportNumber = "LA-UR-19-31717", doi = "10.1038/s41598-020-67769-x", journal = "Sci. Rep.", volume = "10", number = "1", pages = "10915", year = "2020" }
Generative models / density estimation -- Phase space generation @article{Yoon:2020zmb, author = "Yoon, Boram", title = "{A machine learning approach for efficient multi-dimensional integration}", eprint = "2009.06697", archivePrefix = "arXiv", primaryClass = "physics.comp-ph", reportNumber = "LA-UR-20-27161", doi = "10.1038/s41598-021-98392-z", journal = "Sci. Rep.", volume = "11", pages = "18965", year = "2021" }