Summary of Low-rank Adaptation For Spatio-temporal Forecasting, by Weilin Ruan et al.
Low-rank Adaptation for Spatio-Temporal Forecasting
by Weilin Ruan, Wei Chen, Xilin Dang, Jianxiang Zhou, Weichuang Li, Xu Liu, Yuxuan Liang
First submitted to arxiv on: 11 Apr 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 novel framework for improving the accuracy of spatial-temporal forecasting in real-world systems. The existing methods focus on developing complex neural networks, but their performance does not show significant improvement. Moreover, these methods neglect node heterogeneity, making it challenging to handle diverse regional nodes effectively. To address this issue, the authors present ST-LoRA, a low-rank adaptation framework that can be used as an off-the-shelf plugin for existing spatial-temporal prediction models. The framework involves tailoring a node adaptive low-rank layer and a multi-layer residual fusion stacking module to enhance the performance of predictor modules in various models. Experimental results on six real-world traffic datasets and six different types of spatio-temporal prediction models show that ST-LoRA achieves consistent and sustained performance enhancement with minimal additional parameters and training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict future changes in dynamic systems like traffic flow. Current methods focus on building complex networks, but they don’t work well. They also ignore the differences between different locations. The authors create a new tool called ST-LoRA that can be used with existing prediction models to make them better. It does this by adjusting the model for each location and combining the results. The authors test their method on six traffic datasets and six types of prediction models, and it works well. |
Keywords
» Artificial intelligence » Lora » Low rank adaptation
