Graph Neural Networks (GNNs) have demonstrated strong predictive performance across a wide range of applications. However, their increasing deployment has raised critical fairness concerns, as these models can inherit and amplify existing biases. Most existing fairness approaches rely on explicit demographic informatio...
Zi-Chong Wang, Zhi-Peng Yin, Mo Sha et al.· Proceedings of the Thirty-Fi...· 4 citations
Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior. {Existing explanation methods generally fall into two categories: attribution-based explanations, which identify the temporal regions most responsible...
Xu Zheng, Zichuan Liu, Zhuomin Chen et al.· 0 citations
As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predictions has become as crucial as the predictions themselves. Although existing interpretable-by-design forecasters reveal their internal structures, they offer no guaran...
NoiseFlow is presented, a data-efficient domain adaptation framework that leverages noise-aware modeling and flow matching to enable robust cross-domain generalization and exhibits heterogeneous sensitivity to noise, which can be amplified under domain shift.
Ai-Tian Ma, Dongsheng Luo, Mo Sha· International Conference on...· 0 citations
Wireless Mesh Networks (WMNs) are essential for many Internet of Things (IoT) applications, such as industrial automation, environmental monitoring, and smart cities. Today, configuring a WMN to meet its stringent performance requirements remains a significant challenge due to dynamic real-world wireless conditions and...
Aitian Ma, Jean Marco Cruz, Dongsheng Luo et al.· Annual International Compute...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
