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cs.LG2025
Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-Temporal Graph Learning Method for Traffic Flow Forecasting
Feng Wang, Tianxiang Chen, Shuyue Wei +4
Spatio-temporal graphs are powerful tools for modeling complex dependencies in traffic time series. However, the distributed nature of real-world traffic data across multiple stake…
cs.LG2024
Does RoBERTa Perform Better than BERT in Continual Learning: An Attention Sink Perspective
Xueying Bai, Yifan Sun, Niranjan Balasubramanian
Continual learning (CL) aims to train models that can sequentially learn new tasks without forgetting previous tasks' knowledge. Although previous works observed that pre-training…