Green Machine Learning

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UDC.coleccionInvestigación
UDC.conferenceTitleESANN 2023
UDC.departamentoCiencias da Computación e Tecnoloxías da Información
UDC.endPage278
UDC.grupoInvLaboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA)
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación
UDC.startPage269
dc.contributor.authorBolón-Canedo, Verónica
dc.contributor.authorMorán-Fernández, Laura
dc.contributor.authorCancela, Brais
dc.contributor.authorAlonso-Betanzos, Amparo
dc.date.accessioned2026-04-13T12:26:09Z
dc.date.available2026-04-13T12:26:09Z
dc.date.issued2023
dc.descriptionTraballo presentado en: ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 04 - 06, October, 2023
dc.description.abstract[Abstract]: Green machine learning refers to research that is more environmentally friendly and inclusive, not only by producing novel results without increasing the computational cost, but also by ensuring that any researcher with a laptop has the opportunity to perform high-quality research without the need to use expensive cloud servers. Efficient machine learning approaches (especially deep learning) are starting to receive some attention in the research community. This tutorial is concerned with the development of machine learning algorithms that optimize efficiency rather than only accuracy. We provide an overview of this recent field, together with a review of the novel contributions to the ESANN 2023 special session on Green Machine Learning.
dc.description.sponsorshipThis work was supported by the Ministry of Science and Innovation of Spain (Grant PID2019-109238GB-C22 / AEI / 10.13039 / 501100011033) and together with “NextGenerationE”/PRTR (TED2021-130599A-I00) and by Xunta de Galicia (Grants ED431G 2019/01 and ED431C 2022/44).
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01
dc.description.sponsorshipXunta de Galicia; ED431C 2022/44
dc.identifier.citationV. Bolón-Canedo, L. Morán-Fernádez, B. Cancela and A. Alonso-Betanzos, "Green Machine Learning", ESANN 2023 Proceedings - 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2023, p.269-278, https://doi.org/10.14428/esann/2023.ES2023-3
dc.identifier.doi10.14428/esann/2023.ES2023-3
dc.identifier.isbn978-2-87587-088-9
dc.identifier.urihttps://hdl.handle.net/2183/47948
dc.language.isoeng
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLE
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2024/TED2021-130599A-I00/ES/ALGORITMOS DE SELECCIÓN DE CARACTERÍSTICAS VERDES Y RÁPIDOS
dc.relation.urihttps://doi.org/10.14428/esann/2023.ES2023-3
dc.rightsAll rights reserved. This is the published version of the paper, distributed in accordance with ESANN's self-archiving policy, which allows authors to archive their work in any repository provided that full reference is made to the ESANN publication.
dc.rights.accessRightsopen access
dc.subjectGreen machine learning
dc.subjectEfficient deep learning
dc.subjectComputational efficiency
dc.titleGreen Machine Learning
dc.typeconference output
dspace.entity.typePublication
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