Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Пространствено-времеви конволюционни мрежи върху графи× | Mamba (модел с отворено състояние)× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2018 | 2023 |
| Създател≠ | Sijie Yan | Albert Gu |
| Тип | Neural network architecture | Neural network architecture |
| Основополагащ източник≠ | 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 ↗ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ |
| Други названия≠ | ST-GCN, Spatial-Temporal Graph CNN | Mamba, State space models, Selective state space |
| Свързани | 4 | 4 |
| Резюме≠ | 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. | Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power. |
| ScholarGateНабор от данни ↗ |
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