Compara mètodes

	Autoatenció multi-cap	Ajustament BERT	Random Forest
Camp	Aprenentatge profund	Aprenentatge profund	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2017	2019	2001
Autor original	Vaswani, A. et al.	Devlin, J. et al.	Breiman, L.
Tipus	Attention mechanism (Transformer core)	Transfer learning (fine-tuning a pre-trained transformer)	Ensemble (bagging of decision trees)
Font seminal	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Àlies	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention	BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionats	5	5	4
Resum	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.	BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data.	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.