手法を比較

	Multi-Head Self-Attention	XGBoost
分野	深層学習	機械学習
系統	Machine learning	Machine learning
提唱年	2017	2016
提唱者	Vaswani, A. et al.	Chen, T. & Guestrin, C.
種類	Attention mechanism (Transformer core)	Ensemble (gradient-boosted decision trees)
原典	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
別名	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention	XGBoost, extreme gradient boosting, scalable tree boosting
関連	5	5
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.	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.