6 citations · 14 across the 6 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★ 1 cited
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction
Youru Li, Zhenfeng Zhu, Xiaobo Guo +3
Risk prediction, as a typical time series modeling problem, is usually achieved by learning trends in markers or historical behavior from sequence data, and has been widely applied…