השוואת שיטות

	תערובת מומחים	XGBoost
תחום	למידה עמוקה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2017	2016
הוגה השיטה	Shazeer, N. et al.	Chen, T. & Guestrin, C.
סוג	Sparse neural network architecture (conditional computation)	Ensemble (gradient-boosted decision trees)
מקור מכונן	Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
כינויים	Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts	XGBoost, extreme gradient boosting, scalable tree boosting
קשורות	3	5
תקציר	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.