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| Mixture of Experts× | XGBoost× | |
|---|---|---|
| Dziedzina≠ | Uczenie głębokie | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2017 | 2016 |
| Twórca≠ | Shazeer, N. et al. | Chen, T. & Guestrin, C. |
| Typ≠ | Sparse neural network architecture (conditional computation) | Ensemble (gradient-boosted decision trees) |
| Źródło pierwotne≠ | Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538 link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Inne nazwy≠ | Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts | XGBoost, extreme gradient boosting, scalable tree boosting |
| Pokrewne≠ | 3 | 5 |
| Podsumowanie≠ | Mixture of Experts (MoE) is a sparse neural-network architecture, introduced by Shazeer and colleagues in 2017 with the sparsely-gated MoE layer, in which only a subset of expert sub-networks is activated for each input. As seen in models such as Switch Transformer and Mixtral, it holds computation cost fixed even as the total parameter count grows. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateZbiór danych ↗ |
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