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Interpretable deep-learning models to help achieve the Sustainable Development Goals
Vinuesa, Ricardo
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. AI Sustainability Center, Stockholm, Sweden.
ORCID iD:
0000-0001-6570-5499
Sirmacek, Beril
Smart Cities, School of Creative Technology, Saxion University of Applied Sciences, Enschede, Netherlands.
ORCID iD:
0000-0002-0343-5072
2021 (English)
In:
Nature Machine Intelligence, ISSN 2522-5839, Vol. 3, no 11, p. 926-926
Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Springer Nature , 2021. Vol. 3, no 11, p. 926-926
National Category
Educational Sciences
Identifiers
URN:
urn:nbn:se:kth:diva-313135
DOI:
10.1038/s42256-021-00414-y
ISI:
000713519400001
Scopus ID:
2-s2.0-85118433204
OAI: oai:DiVA.org:kth-313135
DiVA, id:
diva2:1670118
Note
QC 20220615
Available from:
2022-06-15
Created:
2022-06-15
Last updated:
2025-02-18
Bibliographically approved
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Vinuesa, Ricardo
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