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| [NEEDS TRANSLATION]× | Grafička mrežna mreža s pažnjom (Graph Attention Network, GAT)× | XGBoost× | |
|---|---|---|---|
| Područje≠ | Duboko učenje | Duboko učenje | Strojno učenje |
| Obitelj | Machine learning | Machine learning | Machine learning |
| Godina nastanka≠ | 2020 | 2018 | 2016 |
| Tvorac≠ | Beltagy, Peters & Cohan (Longformer); Zaheer et al. (BigBird) | Veličković, P. et al. | Chen, T. & Guestrin, C. |
| Vrsta≠ | Sparse-attention Transformer for long sequences | Graph neural network (attention-based) | Ensemble (gradient-boosted decision trees) |
| Temeljni izvor≠ | Beltagy, I., Peters, M. E. & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv. link ↗ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Drugi nazivi≠ | Uzun Dizi Transformer (Longformer / BigBird), uzun dizi transformer, long-document transformer, sparse-attention transformer | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network | XGBoost, extreme gradient boosting, scalable tree boosting |
| Srodne≠ | 4 | 4 | 5 |
| Sažetak≠ | Long-sequence Transformers such as Longformer (Beltagy, Peters & Cohan, 2020) and BigBird (Zaheer et al., 2020) replace the standard Transformer's O(n²) attention with sparse attention patterns that scale linearly, O(n), with sequence length. This lets a single model attend over thousands of tokens — full documents, legal texts, or genomic sequences — that would not fit a conventional Transformer. | 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). | 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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