Summary of Continual Learning For Smart City: a Survey, by Li Yang et al.
Continual Learning for Smart City: A Survey
by Li Yang, Zhipeng Luo, Shiming Zhang, Fei Teng, Tianrui Li
First submitted to arxiv on: 1 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 The paper surveys various machine learning methods for continual learning (CL) in the context of smart city development. CL allows models to adapt to changing environments, where tasks, data, and distributions can vary over time. The survey categorizes numerous basic and advanced CL methods, including graph, spatial-temporal, multi-modal, and federated learning. Applications include transportation, environment, public health, safety, networks, and associated datasets. Challenges and potential research directions are also discussed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines can learn from new information without forgetting what they already know. This is important for cities with lots of data being generated all the time. The authors reviewed many different ways to make this happen, including using graphs, learning about space and time, and combining different types of data. They also looked at how this technology is being used in cities to improve transportation, the environment, public health, and more. |
Keywords
* Artificial intelligence * Continual learning * Federated learning * Machine learning * Multi modal
