A linear-complexity architecture with balanced square partitioning and hierarchical low-rank linear interactions outperforms attention/graph baselines on four large-scale traffic forecasting datasets.
Jensen, Xiaofang Zhou, and Kai Zheng
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SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting
A linear-complexity architecture with balanced square partitioning and hierarchical low-rank linear interactions outperforms attention/graph baselines on four large-scale traffic forecasting datasets.