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1 Ergebnisse
1
Variational Inference for Training Graph Neural Networks in..:
, In:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
,
Lao, Danning
;
Yang, Xinyu
;
Wu, Qitian
. - p. 824-834 , 2022
Link:
https://dl.acm.org/doi/10.1145/3534678.3539283
RT T1
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
: T1
Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation
UL https://suche.suub.uni-bremen.de/peid=acm-3539283&Exemplar=1&LAN=DE A1 Lao, Danning A1 Yang, Xinyu A1 Wu, Qitian A1 Yan, Junchi PB ACM YR 2022 K1 graph neural networks K1 label-efficient learning K1 semi-supervised learning on graphs K1 variational inference K1 Computing methodologies K1 Machine learning K1 Learning settings K1 Semi-supervised learning settings K1 Machine learning approaches K1 Learning in probabilistic graphical models K1 Bayesian network models SP 824 OP 834 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3534678.3539283 DO https://dl.acm.org/doi/10.1145/3534678.3539283 SF ELIB - SuUB Bremen
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