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Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Spatial-Temporal Graph Convolutional Networks× | TimeGPT× | |
|---|---|---|
| Ämnesområde | Djupinlärning | Djupinlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2018 | 2023 |
| Upphovsperson≠ | Sijie Yan | Fabio Garza |
| Typ | Neural network architecture | Neural network architecture |
| Ursprungskälla≠ | Yan, S., Xiong, Y., & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32). link ↗ | Garza, F., & White, C. W. (2023). TimeGPT-1: A Time Series Foundation Model. In ICML 2024 Time Series Workshop. link ↗ |
| Alias | ST-GCN, Spatial-Temporal Graph CNN | TimeGPT-1, Time series GPT |
| Närliggande | 4 | 4 |
| Sammanfattning≠ | Spatial-Temporal Graph Convolutional Networks (ST-GCN) is an architecture introduced by Yan et al. in 2018 for skeleton-based action recognition. By modeling human skeletons as graphs where joints are nodes and bones are edges, ST-GCN applies graph convolutions across space and time to recognize actions from skeleton sequences. | TimeGPT is a time series foundation model introduced by Garza and White in 2023 that unifies forecasting, anomaly detection, and classification in a single pre-trained model. Inspired by large language models, TimeGPT is pre-trained on diverse time series and transfers well to downstream tasks with minimal fine-tuning. |
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