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1 Ergebnisse
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UniST: A Prompt-Empowered Universal Model for Urban Spatio-..:
, In:
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
,
Yuan, Yuan
;
Ding, Jingtao
;
Feng, Jie
.. - p. 4095-4106 , 2024
Link:
https://dl.acm.org/doi/10.1145/3637528.3671662
RT T1
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
: T1
UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
UL https://suche.suub.uni-bremen.de/peid=acm-3671662&Exemplar=1&LAN=DE A1 Yuan, Yuan A1 Ding, Jingtao A1 Feng, Jie A1 Jin, Depeng A1 Li, Yong PB ACM YR 2024 K1 prompt learning K1 spatio-temporal prediction K1 universal model K1 Computing methodologies K1 Machine learning K1 Machine learning approaches SP 4095 OP 4106 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3637528.3671662 DO https://dl.acm.org/doi/10.1145/3637528.3671662 SF ELIB - SuUB Bremen
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