Summary of Coms2t: a Complementary Spatiotemporal Learning System For Data-adaptive Model Evolution, by Zhengyang Zhou et al.
ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution
by Zhengyang Zhou, Qihe Huang, Binwu Wang, Jianpeng Hou, Kuo Yang, Yuxuan Liang, Yang Wang
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 ComS2T model is a prompt-based complementary spatiotemporal learning approach that enables data adaptation and empowers the evolution of models for smart cities and sustainable urban development. By partitioning the neural architecture into stable and dynamic components, ComS2T disentangles two disjoint structures to consolidate historical memory while updating new knowledge. A data-adaptive prompt mechanism is introduced to facilitate fine-tuning of the neural architecture conditioned on prompts, allowing efficient adaptation during testing. This approach is validated through extensive experiments demonstrating its efficacy in adapting to various spatiotemporal out-of-distribution scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ComS2T model is a new way for computers to learn from data that changes over time and space. It’s like how our brains remember things and then update what we know as we learn more. The model has two parts: one that remembers past information and another that learns new things. By using “prompts” that describe the type of data, the model can adapt to new information and make better predictions. This is important for making smart cities and towns sustainable. |
Keywords
* Artificial intelligence * Fine tuning * Prompt * Spatiotemporal
