Summary of Spot-mamba: Learning Long-range Dependency on Spatio-temporal Graphs with Selective State Spaces, by Jinhyeok Choi et al.
SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces
by Jinhyeok Choi, Heehyeon Kim, Minhyeong An, Joyce Jiyoung Whang
First submitted to arxiv on: 17 Jun 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 This paper proposes a new framework for Spatio-Temporal Graph (STG) forecasting called SpoT-Mamba. Building upon recent state-of-the-art methods like Mamba, which excels at capturing long-range dependencies, SpoT-Mamba aims to tackle the challenge of modeling complex dynamics in STGs while improving performance gains. The approach generates node embeddings by scanning various walk sequences and then uses temporal scans to capture long-range spatio-temporal dependencies. Experimental results on a real-world traffic forecasting dataset demonstrate the effectiveness of SpoT-Mamba. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SpoT-Mamba is a new way to predict what will happen in the future based on patterns we see in how things are connected in space and time. It’s like trying to guess where traffic will be heavy tomorrow by looking at how roads are used today. The problem is that these connections can be very complex, so it’s hard to make good predictions. The paper proposes a new way to do this using something called node embeddings and temporal scans. |
