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| Kolmogorov-Arnold Networks× | Mamba (Modello a Spazio degli Stati)× | Autoencoder Mascherati× | |
|---|---|---|---|
| Campo | Apprendimento profondo | Apprendimento profondo | Apprendimento profondo |
| Famiglia | Machine learning | Machine learning | Machine learning |
| Anno di origine≠ | 2024 | 2023 | 2021 |
| Ideatore≠ | Ziming Liu | Albert Gu | Kaiming He |
| Tipo | Neural network architecture | Neural network architecture | Neural network architecture |
| Fonte seminale≠ | 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 ↗ | He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2022). Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16000-16009). DOI ↗ |
| Alias≠ | KAN, Kolmogorov-Arnold | Mamba, State space models, Selective state space | MAE, Vision MAE |
| Correlati | 4 | 4 | 4 |
| Sintesi≠ | 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. | Masked Autoencoders (MAE) is a self-supervised learning approach introduced by He et al. in 2021 that masks random patches of an image and trains a model to reconstruct the missing content. Adapting the masked language modeling paradigm from NLP to vision, MAE learns rich visual representations by solving a challenging reconstruction task without requiring labels. |
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