3 papers
cs.LG2026
Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates
Linxiao Yang, Xue Jiang, Gezheng Xu +9
Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted featur…
cs.LG2025
TriForecaster: A Mixture of Experts Framework for Multi-Region Electric Load Forecasting with Tri-dimensional Specialization
Zhaoyang Zhu, Zhipeng Zeng, Qiming Chen +4
Electric load forecasting is pivotal for power system operation, planning and decision-making. The rise of smart grids and meters has provided more detailed and high-quality load d…
cs.LG2024
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
Luyuan Xie, Manqing Lin, ChenMing Xu +7
In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy. Federated learning facilitates colla…