3 citations · 6 across the 22 of their papers we have counts for
8 papers · 1 filter
Hypergraph-Based Dynamic Graph Node Classification
Xiaoxu Ma, Chen Zhao, Minglai Shao +1
Node classification on static graphs has achieved significant success, but achieving accurate node classification on dynamic graphs where node topology, attributes, and labels chan…
A Dual-Perspective Metaphor Detection Framework Using Large Language Models
Yujie Lin, Jingyao Liu, Yan Gao +2
Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches ofte…
Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action Recognition
Hao Huang, Yujie Lin, Siyu Chen +1
Skeleton-based action recognition using GCNs has achieved remarkable performance, but recognizing ambiguous actions, such as "waving" and "saluting", remains a significant challeng…
MLDGG: Meta-Learning for Domain Generalization on Graphs
Qin Tian, Chen Zhao, Minglai Shao +3
Domain generalization on graphs aims to develop models with robust generalization capabilities, ensuring effective performance on the testing set despite disparities between testin…
Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment
Jia Song, Wanru Zhuang, Yujie Lin +5
Cross-modal text-molecule retrieval model aims to learn a shared feature space of the text and molecule modalities for accurate similarity calculation, which facilitates the rapid…
GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Models
Zhixia He, Chen Zhao, Minglai Shao +3
Out-of-distribution (OOD) detection poses a significant challenge for Graph Neural Networks (GNNs), particularly in open-world scenarios with varying distribution shifts. Most exis…
FADE: Towards Fairness-aware Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models
Yujie Lin, Dong Li, Minglai Shao +2
Fairness-aware domain generalization (FairDG) has emerged as a critical challenge for deploying trustworthy AI systems, particularly in scenarios involving distribution shifts. Tra…
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
Minglai Shao, Dong Li, Chen Zhao +3
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced…