84 citations · 260 across the 21 of their papers we have counts for
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cs.LG2022
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
Ying Chen, Siwei Qiang, Mingming Ha +5
In recent years, semi-supervised graph learning with data augmentation (DA) is currently the most commonly used and best-performing method to enhance model robustness in sparse sce…
cs.LG2022★ 15 cited
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
Jianfei Yang, Xiangyu Peng, Kai Wang +4
Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not req…