Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Rețele Kolmogorov-Arnold× | Mamba (Model de Spațiu de Stări)× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2024 | 2023 |
| Autorul original≠ | Ziming Liu | Albert Gu |
| Tip | Neural network architecture | Neural network architecture |
| Sursa seminală≠ | Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T. Y., & Tegmark, M. (2024). KAN: Kolmogorov-Arnold Networks. arXiv preprint arXiv:2404.19756. link ↗ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ |
| Denumiri alternative≠ | KAN, Kolmogorov-Arnold | Mamba, State space models, Selective state space |
| Înrudite | 4 | 4 |
| Rezumat≠ | Kolmogorov-Arnold Networks (KAN) is a neural network architecture introduced by Liu et al. in 2024 that replaces linear transformations with learned univariate functions on edges. Inspired by the Kolmogorov-Arnold representation theorem, KAN achieves superior function approximation with fewer parameters than traditional MLPs, offering potential efficiency gains and improved interpretability. | 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. |
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