So sánh phương pháp

	Mixture of Experts	Mạng Hồi quy Đồ thị (Graph Attention Network - GAT)	Rừng ngẫu nhiên	XGBoost
Lĩnh vực	Học sâu	Học sâu	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning	Machine learning
Năm ra đời	2017	2018	2001	2016
Người khởi xướng	Shazeer, N. et al.	Veličković, P. et al.	Breiman, L.	Chen, T. & Guestrin, C.
Loại	Sparse neural network architecture (conditional computation)	Graph neural network (attention-based)	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted decision trees)
Công trình gốc	Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Tên gọi khác	Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Liên quan	3	4	4	5
Tóm tắt	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.	The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.