2 papers
cs.LG2026
FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction
Xinjin Li, Jinghan Cao, Mengyue Wang +5
Traffic forecasting requires modeling complex temporal dynamics and long-range spatial dependencies over large sensor networks. Existing methods typically face a trade-off between…
cs.LG2022
Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network
Yan Pang, Chao Liu
Dynamic graph neural networks have been widely used in modeling and representation learning of graph structure data. Current dynamic representation learning focuses on either discr…