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| Mamba (State Space Model)× | N-BEATSx× | Vision Transformer× | |
|---|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2023 | 2023 | 2021 |
| Urheber≠ | Albert Gu | Cristian Challu | Dosovitskiy, A. et al. |
| Typ≠ | Neural network architecture | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| Wegweisende Quelle≠ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ | 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 ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Aliasnamen≠ | Mamba, State space models, Selective state space | N-BEATSx, NBEATS-x | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Verwandt≠ | 4 | 4 | 5 |
| Zusammenfassung≠ | 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. | 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. | The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs). |
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