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Editorial: High-performance tensor computations in scientific computing and data science
Simulation and Data Lab Quantum Materials, Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany.
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-4972-7097
Department of Computer Science, North Carolina State University, NC, Raleigh, USA.
Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany.
2022 (English)In: Frontiers in Applied Mathematics and Statistics, E-ISSN 2297-4687, Vol. 8, article id 1038885Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
Frontiers Media S.A., 2022. Vol. 8, article id 1038885
Keywords [en]
Deep Learning, high performance optimization, low-rank approximation, multilinear algebra, tensor decomposition, tensor library, tensor network, tensor operation
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-200670DOI: 10.3389/fams.2022.1038885ISI: 000869830000001Scopus ID: 2-s2.0-85140088303OAI: oai:DiVA.org:umu-200670DiVA, id: diva2:1707915
Available from: 2022-11-02 Created: 2022-11-02 Last updated: 2023-11-10Bibliographically approved

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fulltext(111 kB)181 downloads
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Bientinesi, Paolo

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