手法を比較

	Multi-Head Self-Attention	ランダムフォレスト
分野	深層学習	機械学習
系統	Machine learning	Machine learning
提唱年	2017	2001
提唱者	Vaswani, A. et al.	Breiman, L.
種類	Attention mechanism (Transformer core)	Ensemble (bagging of decision trees)
原典	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
別名	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
関連	5	4
概要	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.