Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| N-BEATSx× | Spatial-Temporal Graph Convolutional Networks× | Vision Mamba× | |
|---|---|---|---|
| Fagfelt | Dyp læring | Dyp læring | Dyp læring |
| Familie | Machine learning | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2023 | 2018 | 2024 |
| Opphavsperson≠ | Cristian Challu | Sijie Yan | Li Zhu |
| Type | Neural network architecture | Neural network architecture | Neural network architecture |
| Opprinnelig kilde≠ | Challu, C., Olivares, K. Q., Oreshkin, B., Garza, F., Mergenthaler-Canseco, M., & Dubrawski, A. (2023). N-BEATSx: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. In ICLR 2023 Workshop on Multimodal Learning for Science (p. 4). link ↗ | 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 ↗ | Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning. link ↗ |
| Alias | N-BEATSx, NBEATS-x | ST-GCN, Spatial-Temporal Graph CNN | ViM, Mamba for Vision |
| Relaterte | 4 | 4 | 4 |
| Sammendrag≠ | N-BEATSx is an extension of the N-BEATS neural time series forecasting model that incorporates exogenous (external) variables through a cross-learner architecture. Published in 2023, N-BEATSx improves upon N-BEATS by enabling the model to leverage additional features beyond the historical time series values. | 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. | Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity. |
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