2 citations · 3 across the 9 of their papers we have counts for
9 papers
Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
Xingcheng Fu, Yisen Gao, Beining Yang +4
Dataset condensation has significantly improved model training efficiency, but its application on devices with different computing power brings new requirements for different data…
FedRGL: Robust Federated Graph Learning for Label Noise
De Li, Haodong Qian, Qiyu Li +4
Federated Graph Learning (FGL) is a distributed machine learning paradigm based on graph neural networks, enabling secure and collaborative modeling of local graph data among clien…
Instant Resonance: Dual Strategy Enhances the Data Consensus Success Rate of Blockchain Threshold Signature Oracles
Youquan Xian, Xueying Zeng, Chunpei Li +4
With the rapid development of Decentralized Finance (DeFi) and Real-World Assets (RWA), the importance of blockchain oracles in real-time data acquisition has become increasingly p…
Personalized federated learning based on feature fusion
Wolong Xing, Zhenkui Shi, Hongyan Peng +2
Federated learning enables distributed clients to collaborate on training while storing their data locally to protect client privacy. However, due to the heterogeneity of data, mod…
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective
De Li, Xianxian Li, Zeming Gan +3
Graph neural networks based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when nois…
Explicit Visual Prompts for Visual Object Tracking
Liangtao Shi, Bineng Zhong, Qihua Liang +3
How to effectively exploit spatio-temporal information is crucial to capture target appearance changes in visual tracking. However, most deep learning-based trackers mainly focus o…