Summary of Get Rid Of Isolation: a Continuous Multi-task Spatio-temporal Learning Framework, by Zhongchao Yi et al.
Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
by Zhongchao Yi, Zhengyang Zhou, Qihe Huang, Yanjiang Chen, Liheng Yu, Xu Wang, Yang Wang
First submitted to arxiv on: 14 Oct 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 A novel Continuous Multi-task Spatio-Temporal learning framework (CMuST) is proposed to empower collective urban intelligence by reforming the urban spatiotemporal learning from single-domain to cooperatively multi-dimensional and multi-task learning. CMuST introduces a new multi-dimensional spatiotemporal interaction network (MSTI) that allows cross-interactions between context and main observations, as well as self-interactions within spatial and temporal aspects. To ensure continuous task learning, a Rolling Adaptation training scheme (RoAda) is devised, which preserves task uniqueness by constructing data summarization-driven task prompts and harnesses correlated patterns among tasks by iterative model behavior modeling. The CMuST framework outperforms existing SOAT methods on both few-shot streaming data and new domain tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Urban intelligence can be enhanced by a Continuous Multi-task Spatio-Temporal learning framework (CMuST). This approach helps urban systems learn from different types of data and adapt to changing conditions. CMuST uses a special network that connects different pieces of information and allows the model to learn from multiple tasks at once. The system is tested on real-world data from three cities and shows better results than other approaches. |
Keywords
» Artificial intelligence » Few shot » Multi task » Spatiotemporal » Summarization
