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Hyperseed: Unsupervised Learning With Vector Symbolic Architectures
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0069-640x
Centre for Data Analytics and Cognition (CDAC), La Trobe University, Melbourne, VIC, Australia.
Centre for Data Analytics and Cognition (CDAC), La Trobe University, Melbourne, VIC, Australia.
Centre for Data Analytics and Cognition (CDAC), La Trobe University, Melbourne, VIC, Australia.
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2024 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 35, no 5, p. 6583-6597Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE , 2024. Vol. 35, no 5, p. 6583-6597
Keywords [en]
Hyperseed, neuromorphic hardware, self-organizing maps (SOMs), vector symbolic architectures (VSAs)
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-94924DOI: 10.1109/TNNLS.2022.3211274ISI: 000890842400001PubMedID: 36383581Scopus ID: 2-s2.0-85142777444OAI: oai:DiVA.org:ltu-94924DiVA, id: diva2:1720742
Funder
The Swedish Foundation for International Cooperation in Research and Higher Education (STINT), MG2020-8842
Note

Validerad;2024;Nivå 2;2024-05-21 (joosat);

Funder: Intel Neuro-morphic Research Community Grant to the Luleå University of Technology; Russian Science Foundation during the period of 2020–2021 (Grant 20-71-10116); Centre for Data Analytics and Cognition (CDAC); European Union’s Horizon 2020 Research and Innovation Program, Marie Skłodowska-Curie (Grant 839179);

Available from: 2022-12-20 Created: 2022-12-20 Last updated: 2024-05-21Bibliographically approved

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Osipov, Evgeny

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