Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Grafu uzmanības tīkls (Graph Attention Network, GAT)× | Atkārtotais neironu tīkls× | XGBoost× | |
|---|---|---|---|
| Nozare≠ | Dziļā mācīšanās | Dziļā mācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2018 | 1986–1990 | 2016 |
| Autors≠ | Veličković, P. et al. | Rumelhart, D. E.; Elman, J. L. | Chen, T. & Guestrin, C. |
| Tips≠ | Graph neural network (attention-based) | Sequential neural network | Ensemble (gradient-boosted decision trees) |
| Pirmavots≠ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Citi nosaukumi≠ | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network | RNN, Elman network, Jordan network, simple recurrent network | XGBoost, extreme gradient boosting, scalable tree boosting |
| Saistītās≠ | 4 | 3 | 5 |
| Kopsavilkums≠ | 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). | A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models. | 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. |
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