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Benchmarking TMVA package against Tensor Flow on event-by-event inference performance on multi-layered perceptrons for HEP

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dc.contributor Conseil Européen pour la Recherche Nucléaire
dc.contributor.author BURLACU, Alexandru
dc.contributor.author WUNSCH, Stefan
dc.contributor.author MONETA, Lorenzo
dc.date.accessioned 2022-02-17T07:54:40Z
dc.date.available 2022-02-17T07:54:40Z
dc.date.issued 2018
dc.identifier.citation BURLACU, Alexandru, WUNSCH, Stefan, MONETA Lorenzo. Benchmarking TMVA package against Tensor Flow on event-by-event inference performance on multi-layered perceptrons for HEP. In: a CERN Summer Student Technical Report, 2018, pp. 1-8. en_US
dc.identifier.uri https://cds.cern.ch/record/2641377
dc.identifier.uri http://repository.utm.md/handle/5014/19403
dc.description.abstract HEP has some very specific requirements about the usage of deep learning. Also, HEP is known for outrageous amounts of data produced in a single collision. For example, at LHC we are talking about petabyte per second scales, and with the introduction of HL-LHC, this numbers will grow significantly. Currently, the HEP community wants to find out, is it possible to efficiently run neural networks in low-level triggers, thus reducing the amount of collected data without compromising its quality? This work aims to find answers to this question and the future directions of current deep network tools used in HEP. This work is a technical report on the project I was working between 2nd in July and 24rd of August 2018. en_US
dc.language.iso en en_US
dc.publisher Conseil Européen pour la Recherche Nucléaire en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject neural networks en_US
dc.subject deep network tools en_US
dc.subject multi-layered perceptrons en_US
dc.subject perceptrons en_US
dc.title Benchmarking TMVA package against Tensor Flow on event-by-event inference performance on multi-layered perceptrons for HEP en_US
dc.type Technical Report en_US


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