Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| N-BEATSx× | Mamba (модел с отворено състояние)× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване | 2023 | 2023 |
| Създател≠ | Cristian Challu | Albert Gu |
| Тип | Neural network architecture | Neural network architecture |
| Основополагащ източник≠ | 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 ↗ |
| Други названия≠ | N-BEATSx, NBEATS-x | Mamba, State space models, Selective state space |
| Свързани | 4 | 4 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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