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dc.contributor.authorMartín Guerrero, José
dc.contributor.authorLisboa, Paulo J G
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-12-01T10:08:53Z
dc.date.available2016-12-01T10:08:53Z
dc.date.issued2016
dc.identifier.citationMartín, J., Lisboa, P., Vellido, A. Physics and machine learning: Emerging paradigms. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016". Bruges: I6doc.com, 2016, p. 319-326.
dc.identifier.isbn978-287587027-8
dc.identifier.urihttp://hdl.handle.net/2117/97581
dc.description.abstractCurrent research in Machine Learning (ML) combines the study of variations on well-established methods with cutting-edge breakthroughs based on completely new approaches. Among the latter, emerging paradigms from Physics have taken special relevance in recent years. Although still in its initial stages, Quantum Machine Learning (QML) shows promising ways to speed up some of the costly ML calculations with a similar or even better performance than existing approaches. Two additional advantages are related to the intrinsic probabilistic approach of QML, since quantum states are genuinely probabilistic, and to the capability of finding the global optimum of a given cost function by means of adiabatic quantum optimization, thus circumventing the usual problem of local minima. Another Physics approach for ML comes from Statistical Physics and is linked to Information theory in supervised and semi-supervised learning frameworks. On the other hand, and from the perspective of Physics, ML can provide solutions by extracting knowledge from huge amounts of data, as it is common in many experiments in the field, such as those related to High Energy Physics for elementary-particle research and Observational Astronomy.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherI6doc.com
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.otherComputation theory
dc.subject.otherCost functions
dc.subject.otherHigh energy physics
dc.subject.otherInformation theory
dc.subject.otherLearning algorithms
dc.subject.otherMultiprocessing systems
dc.subject.otherNeural networks
dc.subject.otherQuantum theory
dc.subject.otherSupervised learning
dc.subject.otherGlobal optimum
dc.subject.otherLocal minimums
dc.subject.otherNew approaches
dc.subject.otherProbabilistic approaches
dc.subject.otherQuantum machines
dc.subject.otherQuantum optimization
dc.subject.otherSemi- supervised learning
dc.subject.otherStatistical physics
dc.titlePhysics and machine learning: Emerging paradigms
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-20.pdf
dc.rights.accessOpen Access
local.identifier.drac19287393
dc.description.versionPostprint (published version)
local.citation.authorMartín, J.; Lisboa, P.; Vellido, A.
local.citation.contributorEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
local.citation.pubplaceBruges
local.citation.publicationNameESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016
local.citation.startingPage319
local.citation.endingPage326


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