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dc.contributor.authorTurvill, Danielle
dc.contributor.authorBarnby, Lee
dc.contributor.authorYuan, Bo
dc.contributor.authorZahir, Ali
dc.date.accessioned2021-02-08T15:09:13Z
dc.date.available2021-02-08T15:09:13Z
dc.date.issued2020-12-28
dc.identifier.citationTurvill, D., Barnby, L., Yuan, B. and Zahir, A., (2020). 'A survey of interpretability of machine learning in accelerator-based high energy physics'. IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, Leicester, 7-9 December. New York: IEEE, pp. 77-86.en_US
dc.identifier.isbn9780738123967
dc.identifier.doi10.1109/bdcat50828.2020.00025
dc.identifier.urihttp://hdl.handle.net/10545/625604
dc.description.abstractData intensive studies in the domain of accelerator-based High Energy Physics, HEP, have become increasingly more achievable due to the emergence of machine learning with high-performance computing and big data technologies. In recent years, the intricate nature of physics tasks and data has prompted the use of more complex learning methods. To accurately identify physics of interest, and draw conclusions against proposed theories, it is crucial that these machine learning predictions are explainable. For it is not enough to accept an answer based on accuracy alone, but it is important in the process of physics discovery to understand exactly why an output was generated. That is, completeness of a solution is required. In this paper, we survey the application of machine learning methods to a variety of accelerator-based tasks in a bid to understand what role interpretability plays within this area. The main contribution of this paper is to promote the need for explainable artificial intelligence, XAI, for the future of machine learning in HEP.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/abstract/document/9302535en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.source2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
dc.subjectExplainable Artificial Intelligenceen_US
dc.subjectDeep Learningen_US
dc.subjectHigh Energy Physicsen_US
dc.subjectMachine Learningen_US
dc.titleA survey of interpretability of machine learning in accelerator-based high energy physicsen_US
dc.typeMeetings and Proceedingsen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journal2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)en_US
dcterms.dateAccepted2020-10-30
dc.author.detailSTF1867en_US


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Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International