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1 Ergebnisse
1
GDOD : Effective Gradient Descent using Orthogonal Decom..:
, In:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management
,
Dong, Xin
;
Wu, Ruize
;
Xiong, Chao
... - p. 386-395 , 2022
Link:
https://dl.acm.org/doi/10.1145/3511808.3557333
RT T1 <cTitle>Proceedings of the 31st ACM International Conference on Information & Knowledge Management</cTitle>: T1 <Title>GDOD : Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3557333&Exemplar=1&LAN=DE A1 Dong, Xin A1 Wu, Ruize A1 Xiong, Chao A1 Li, Hai A1 Cheng, Lei A1 He, Yong A1 Qian, Shiyou A1 Cao, Jian A1 Mo, Linjian PB ACM YR 2022 K1 gradient conflict K1 multi-task learning K1 orthogonal decomposition K1 Computing methodologies K1 Machine learning K1 Learning paradigms K1 Multi-task learning K1 Transfer learning SP 386 OP 395 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3511808.3557333 DO https://dl.acm.org/doi/10.1145/3511808.3557333 SF ELIB - SuUB Bremen
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