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Physics and machine learning: Emerging paradigms
dc.contributor.author | Martín Guerrero, José |
dc.contributor.author | Lisboa, Paulo J G |
dc.contributor.author | Vellido Alcacena, Alfredo |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2016-12-01T10:08:53Z |
dc.date.available | 2016-12-01T10:08:53Z |
dc.date.issued | 2016 |
dc.identifier.citation | Martín, 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.isbn | 978-287587027-8 |
dc.identifier.uri | http://hdl.handle.net/2117/97581 |
dc.description.abstract | Current 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.extent | 8 p. |
dc.language.iso | eng |
dc.publisher | I6doc.com |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Machine learning |
dc.subject.other | Computation theory |
dc.subject.other | Cost functions |
dc.subject.other | High energy physics |
dc.subject.other | Information theory |
dc.subject.other | Learning algorithms |
dc.subject.other | Multiprocessing systems |
dc.subject.other | Neural networks |
dc.subject.other | Quantum theory |
dc.subject.other | Supervised learning |
dc.subject.other | Global optimum |
dc.subject.other | Local minimums |
dc.subject.other | New approaches |
dc.subject.other | Probabilistic approaches |
dc.subject.other | Quantum machines |
dc.subject.other | Quantum optimization |
dc.subject.other | Semi- supervised learning |
dc.subject.other | Statistical physics |
dc.title | Physics and machine learning: Emerging paradigms |
dc.type | Conference report |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-20.pdf |
dc.rights.access | Open Access |
local.identifier.drac | 19287393 |
dc.description.version | Postprint (published version) |
local.citation.author | Martín, J.; Lisboa, P.; Vellido, A. |
local.citation.contributor | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
local.citation.pubplace | Bruges |
local.citation.publicationName | ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016 |
local.citation.startingPage | 319 |
local.citation.endingPage | 326 |