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Contextual Self-attentive Temporal Point Process for Physic..:

, In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,

Yang, Fangkai ; Zhang, Jue ; Wang, Lu... - p. 5372-5381 , 2023

Link: https://dl.acm.org/doi/10.1145/3580305.3599794

RT 
T1 <cTitle>Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining</cTitle>: T1 <Title>Contextual Self-attentive Temporal Point Process for Physical Decommissioning Prediction of Cloud Assets</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3599794&Exemplar=1&LAN=DE
				
A1 Yang, Fangkai
A1 Zhang, Jue
A1 Wang, Lu
A1 Qiao, Bo
A1 Weng, Di
A1 Qin, Xiaoting
A1 Weber, Gregory
A1 Das, Durgesh Nandini
A1 Rakhunathan, Srinivasan
A1 Srikanth, Ranganathan
A1 Lin, Qingwei
A1 Zhang, Dongmei
PB ACM
YR 2023
K1 cloud asset decommission
K1 deep learning
K1 sequence prediction
K1 temporal point process
K1 Information systems
K1 Information systems applications
K1 Data mining
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
K1 Social and professional topics
K1 Professional topics
K1 Computing industry
K1 Sustainability
SP 5372
OP 5381
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3580305.3599794
DO https://dl.acm.org/doi/10.1145/3580305.3599794
SF ELIB - SuUB Bremen

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