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Machine Learning in High Energy Physics Community White Paper / Albertsson, Kim (Lulea U.) ; Altoe, Piero (NVIDIA, Santa Clara) ; Anderson, Dustin (Caltech) ; Anderson, John ; Andrews, Michael (Carnegie Mellon U.) ; Araque Espinosa, Juan Pedro (LIP, Lisbon) ; Aurisano, Adam (Cincinnati U.) ; Basara, Laurent (INFN, Padua ; Padua U.) ; Bevan, Adrian (University Coll. London) ; Bhimji, Wahid (LBL, Berkeley) et al.
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. [...]
arXiv:1807.02876; FERMILAB-PUB-18-318-CD-DI-PPD.- 2018-10-18 - 27 p. - Published in : J. Phys.: Conf. Ser. 1085 (2018) 022008 Fulltext: 1807.02876 - PDF; Albertsson_2018_J._Phys.__Conf._Ser._1085_022008 - PDF; fulltext1681439 - PDF; fermilab-pub-18-318-cd-di-ppd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server (fulltext available)
In : 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA, USA, 21 - 25 Aug 2017, pp.022008

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39	Kaul, G L

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