Summary of Spatio-temporal Few-shot Learning Via Diffusive Neural Network Generation, by Yuan Yuan et al.
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
by Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin, Yong Li
First submitted to arxiv on: 19 Feb 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 generative pre-training framework, GPD, tackles the issue of data scarcity in smart city applications by enabling spatio-temporal few-shot learning with urban knowledge transfer. Unlike conventional approaches, GPD takes a novel approach by performing generative pre-training on a collection of neural network parameters optimized with data from source cities. This allows for adaptability to diverse data distributions and city-specific characteristics. The framework employs a Transformer-based denoising diffusion model, which is model-agnostic to integrate with powerful spatio-temporal neural networks. GPD consistently outperforms state-of-the-art baselines on multiple real-world datasets for tasks such as traffic speed prediction and crowd flow prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GPD helps smart cities by fixing a big problem: not enough data! Usually, we need lots of information to train models, but that’s hard when there isn’t much data available. GPD solves this by using old data from other cities to help create new models. This way, even if there’s little data in your city, you can still use GPD to make good predictions. It’s like having a super smart friend who knows lots of things and can help you figure out what will happen in the future. |
Keywords
* Artificial intelligence * Diffusion model * Few shot * Neural network * Transformer
