65 citations · 150 across the 35 of their papers we have counts for
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cs.LG2025
GMoPE:A Prompt-Expert Mixture Framework for Graph Foundation Models
Zhibin Wang, Zhixing Zhang, Shuqi Wang +2
Graph Neural Networks (GNNs) have demonstrated impressive performance on task-specific benchmarks, yet their ability to generalize across diverse domains and tasks remains limited.…
cs.LG2023★ 6 cited
Data Pruning via Moving-one-Sample-out
Haoru Tan, Sitong Wu, Fei Du +4
In this paper, we propose a novel data-pruning approach called moving-one-sample-out (MoSo), which aims to identify and remove the least informative samples from the training set.…
cs.LG2022★ 1 cited
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization
Mengzhu Wang, Jianlong Yuan, Qi Qian +2
Domain generalization (DG) aims to learn a model on one or more different but related source domains that could be generalized into an unseen target domain. Existing DG methods try…