Confronta i metodi

	Longformer / BigBird	Rete di Attenzione su Grafo	Mixture of Experts	XGBoost
Campo	Apprendimento profondo	Apprendimento profondo	Apprendimento profondo	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning	Machine learning
Anno di origine	2020	2018	2017	2016
Ideatore	Beltagy, Peters & Cohan (Longformer); Zaheer et al. (BigBird)	Veličković, P. et al.	Shazeer, N. et al.	Chen, T. & Guestrin, C.
Tipo	Sparse-attention Transformer for long sequences	Graph neural network (attention-based)	Sparse neural network architecture (conditional computation)	Ensemble (gradient-boosted decision trees)
Fonte seminale	Beltagy, I., Peters, M. E. & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv.	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	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	Uzun Dizi Transformer (Longformer / BigBird), uzun dizi transformer, long-document transformer, sparse-attention transformer	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	XGBoost, extreme gradient boosting, scalable tree boosting
Correlati	4	4	3	5
Sintesi	Long-sequence Transformers such as Longformer (Beltagy, Peters & Cohan, 2020) and BigBird (Zaheer et al., 2020) replace the standard Transformer's O(n²) attention with sparse attention patterns that scale linearly, O(n), with sequence length. This lets a single model attend over thousands of tokens — full documents, legal texts, or genomic sequences — that would not fit a conventional Transformer.	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.	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.