Summary of Easyst: a Simple Framework For Spatio-temporal Prediction, by Jiabin Tang et al.
EasyST: A Simple Framework for Spatio-Temporal Prediction
by Jiabin Tang, Wei Wei, Lianghao Xia, Chao Huang
First submitted to arxiv on: 10 Sep 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 EasyST paradigm is a simple framework for spatio-temporal prediction that learns lightweight and robust Multi-Layer Perceptrons (MLPs) by effectively distilling knowledge from complex Graph Neural Networks (GNNs). The framework addresses scalability and generalization challenges in large-scale datasets, enabling improved performance in urban sensing scenarios. By integrating the spatio-temporal information bottleneck with teacher-bounded regression loss, EasyST filters out task-irrelevant noise and avoids erroneous guidance. Additionally, spatial and temporal prompts are incorporated to provide downstream task contexts, enhancing the generalization ability of the student model. Experimental results on three spatio-temporal datasets demonstrate that EasyST surpasses state-of-the-art approaches in terms of efficiency and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EasyST is a new way to predict what will happen in cities over time. It’s like trying to forecast traffic or weather, but for entire cities! Right now, computers have trouble learning from all the data they collect because it gets too complicated. EasyST solves this problem by making a simpler model that can learn from more complex models. This helps the computer be more accurate and efficient when predicting what will happen in cities. The results show that EasyST is better than other methods at doing this! |
Keywords
» Artificial intelligence » Generalization » Regression » Student model
