Compară metode

	Rețeaua de Atenție Grafică	Amestec de Experți	Pădurea Aleatoare (Random Forest)
Domeniu	Învățare profundă	Învățare profundă	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	2018	2017	2001
Autorul original	Veličković, P. et al.	Shazeer, N. et al.	Breiman, L.
Tip	Graph neural network (attention-based)	Sparse neural network architecture (conditional computation)	Ensemble (bagging of decision trees)
Sursa seminală	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Denumiri alternative	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Înrudite	4	3	4
Rezumat	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).	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.	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.