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| Multi-Head Self-Attention× | Random Forest× | |
|---|---|---|
| Dziedzina≠ | Uczenie głębokie | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2017 | 2001 |
| Twórca≠ | Vaswani, A. et al. | Breiman, L. |
| Typ≠ | Attention mechanism (Transformer core) | Ensemble (bagging of decision trees) |
| Źródło pierwotne≠ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Inne nazwy | Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5. | 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. |
| ScholarGateZbiór danych ↗ |
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