Sammenlign metoder
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| N-BEATSx× | Mamba (State Space Model)× | Rumlig-tidslige graf-konvolutionelle netværk× | |
|---|---|---|---|
| Fagområde | Dyb læring | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning | Machine learning |
| Oprindelsesår≠ | 2023 | 2023 | 2018 |
| Ophavsperson≠ | Cristian Challu | Albert Gu | Sijie Yan |
| Type | Neural network architecture | Neural network architecture | Neural network architecture |
| Oprindelig 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 ↗ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. 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 ↗ |
| Aliasser≠ | N-BEATSx, NBEATS-x | Mamba, State space models, Selective state space | ST-GCN, Spatial-Temporal Graph CNN |
| Relaterede | 4 | 4 | 4 |
| Resumé≠ | 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. | 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. | 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. |
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