Summary of Adaptive Least Mean Squares Graph Neural Networks and Online Graph Signal Estimation, by Yi Yan et al.
Adaptive Least Mean Squares Graph Neural Networks and Online Graph Signal Estimation
by Yi Yan, Changran Peng, Ercan Engin Kuruoglu
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 Adaptive Least Mean Squares Graph Neural Networks (LMS-GNN) architecture efficiently estimates time-varying graph signals from noisy partial observations, capturing time variation and bridging cross-space-time interactions. Combining adaptive graph filters and Graph Neural Networks, LMS-GNN updates filter coefficients via backpropagation at each time step. Our method outperforms graph-based methods like adaptive graph filters and graph convolutional neural networks on real-world temperature data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to predict patterns in data that changes over time and space. They combined two types of artificial intelligence models to make it work. This approach is better than other methods at predicting temperature data, which can be useful for many applications. |
Keywords
* Artificial intelligence * Backpropagation * Gnn * Temperature
