Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Mixture of Experts× | Rete di Attenzione su Grafo× | |
|---|---|---|
| Campo | Apprendimento profondo | Apprendimento profondo |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2017 | 2018 |
| Ideatore≠ | Shazeer, N. et al. | Veličković, P. et al. |
| Tipo≠ | Sparse neural network architecture (conditional computation) | Graph neural network (attention-based) |
| Fonte seminale≠ | Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538 link ↗ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| Alias≠ | 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 |
| Correlati≠ | 3 | 4 |
| Sintesi≠ | 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). |
| ScholarGateInsieme di dati ↗ |
|
|