قارن الطرق

	شبكة الانتباه الرسومية	مزيج الخبراء
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2018	2017
صاحب الطريقة	Veličković, P. et al.	Shazeer, N. et al.
النوع	Graph neural network (attention-based)	Sparse neural network architecture (conditional computation)
المصدر التأسيسي	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
الأسماء البديلة	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
ذات صلة	4	3
الملخص	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.