Summary of Linear Attention Is Enough in Spatial-temporal Forecasting, by Xinyu Ning
Linear Attention is Enough in Spatial-Temporal Forecasting
by Xinyu Ning
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, STformer, addresses the limitations of existing spatial-temporal forecasting methods by treating nodes in road networks at different time steps as independent spatial-temporal tokens and feeding them into a vanilla Transformer. This design achieves state-of-the-art (SOTA) performance on traffic datasets while maintaining an affordable computational cost. The authors also introduce a variant, NSTformer, based on the Nyström method to approximate self-attention with linear complexity, which performs slightly better than STformer in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For predicting traffic flow, researchers have developed methods that treat road networks as spatial-temporal graphs. However, these approaches struggle to capture the dynamic topology of roads and learn spatial and temporal relationships separately. The new method, called STformer, solves this problem by representing nodes at different time steps as independent tokens and feeding them into a Transformer model. This design achieves better results than previous methods while using less computational power. |
Keywords
» Artificial intelligence » Self attention » Transformer
