Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Mixture of Experts× | Graph Attention Network× | Random Forest× | |
|---|---|---|---|
| Vakgebied≠ | Deep learning | Deep learning | Machine learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2017 | 2018 | 2001 |
| Grondlegger≠ | Shazeer, N. et al. | Veličković, P. et al. | Breiman, L. |
| Type≠ | Sparse neural network architecture (conditional computation) | Graph neural network (attention-based) | Ensemble (bagging of decision trees) |
| Oorspronkelijke bron≠ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Aliassen≠ | 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 | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Verwant≠ | 3 | 4 | 4 |
| Samenvatting≠ | 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). | 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. |
| ScholarGateGegevensset ↗ |
|
|
|