Achieving fairness in machine learning models while maintaining high accuracy is an important but complex task, especially when handling multiple sensitive attributes. Traditional fairness methods often struggle to eliminate bias within subgroups divided by sensitive attributes. Several key challenges have been identif...
Heng-Yu Yue, Fan Wang, Wei-Ming Liu et al.· Proceedings of the Thirty-Fi...· 0 citations
This work introduces Slow-OCast, a transfer-learning based model designed for high-resolution ocean environmental forecasting that incorporates the slow-varying motion characteristics of the ocean and comprises two insightful modules.
Qi-Xiu Li, Xiang Zhu, Xiao-Yong Li et al.· Proceedings of the 32nd ACM...· 0 citations
Recommendation plays a crucial role in the modern Web ecosystem, powering personalized services across e-commerce, social platforms, and online content networks. To model complex user–item interactions in such Web environments, Graph Neural Networks (GNNs) have become a popular and effective approach due to their abili...
Yuwen Liu, Lianyong Qi, Xucheng Zhou et al.· Annual International ACM SIG...· 0 citations
Regional high-resolution ocean environmental forecasting combines spatial numerical modeling with temporal prediction, and is essential for monitoring the ecological security of specific ocean regions. In recent years, deep learning methods are generally more computationally efficient than traditional numerical models...
Qixiu Li, Xiang Zhu, Xiaoyong Li et al.· Proceedings of the 32nd ACM...· 0 citations
A novel cloud–edge collaborative intelligence framework which enables synergy between large and small models for STWPF and outperforms state-of-the-art baselines, highlighting the practical value of the framework.
Zhiqiang Jiang, Changfu You, Dong Ma et al.· Journal of Cloud Computing· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
