2 citations · 5 across the 5 of their papers we have counts for
5 papers
Tuning Pre-trained Model via Moment Probing
Mingze Gao, Qilong Wang, Zhenyi Lin +3
Recently, efficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in expl…
Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module
Jingbo Zhou, Yixuan Du, Ruqiong Zhang +7
Graph Neural Networks (GNNs), a type of neural network that can learn from graph-structured data through neighborhood information aggregation, have shown superior performance in va…
Adaptive Depth Graph Attention Networks
Jingbo Zhou, Yixuan Du, Ruqiong Zhang +1
As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely…
Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning
Dongjie Wang, Lingfei Wu, Denghui Zhang +3
The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive.…
Robust Training of Graph Neural Networks via Noise Governance
Siyi Qian, Haochao Ying, Renjun Hu +4
Graph Neural Networks (GNNs) have become widely-used models for semi-supervised learning. However, the robustness of GNNs in the presence of label noise remains a largely under-exp…