Methoden vergleichen
Prüfen Sie die ausgewählten Methoden nebeneinander; abweichende Zeilen sind hervorgehoben.
| Graph Attention Network× | Mixture of Experts× | Random Forest× | |
|---|---|---|---|
| Fachgebiet≠ | Deep Learning | Deep Learning | Maschinelles Lernen |
| Familie | Machine learning | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2018 | 2017 | 2001 |
| Urheber≠ | Veličković, P. et al. | Shazeer, N. et al. | Breiman, L. |
| Typ≠ | Graph neural network (attention-based) | Sparse neural network architecture (conditional computation) | Ensemble (bagging of decision trees) |
| Wegweisende Quelle≠ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Aliasnamen≠ | 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 | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Verwandt≠ | 4 | 3 | 4 |
| Zusammenfassung≠ | 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. | 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. |
| ScholarGateDatensatz ↗ |
|
|
|