Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Red de Atención Gráfica× | Mezcla de Expertos× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2018 | 2017 |
| Autor original≠ | Veličković, P. et al. | Shazeer, N. et al. |
| Tipo≠ | Graph neural network (attention-based) | Sparse neural network architecture (conditional computation) |
| Fuente seminal≠ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538 link ↗ |
| Alias≠ | 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 |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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