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| Kolmogorov-Arnold hálózatok× | Mamba (Állapot-tér modell)× | Neuronális Sugárzási Mezők (NeRF)× | |
|---|---|---|---|
| Tudományterület | Mélytanulás | Mélytanulás | Mélytanulás |
| Módszercsalád | Machine learning | Machine learning | Machine learning |
| Keletkezés éve≠ | 2024 | 2023 | 2020 |
| Megalkotó≠ | Ziming Liu | Albert Gu | Ben Mildenhall |
| Típus | Neural network architecture | Neural network architecture | Neural network architecture |
| Alapmű≠ | 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 ↗ | Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In Computer Vision-ECCV 2020: 16th European Conference (pp. 405-421). Springer International Publishing. DOI ↗ |
| Alternatív nevek≠ | KAN, Kolmogorov-Arnold | Mamba, State space models, Selective state space | NeRF, Neural radiance field |
| Kapcsolódó | 4 | 4 | 4 |
| Összefoglaló≠ | 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. | Neural Radiance Fields (NeRF) is a method introduced by Mildenhall et al. in 2020 that represents a 3D scene as a continuous function parameterized by a neural network. Given multi-view images of a scene, NeRF learns to predict the color and density of light rays at any spatial location and viewing angle, enabling novel view synthesis with photorealistic quality. |
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