手法を比較

	ニューラルアーキテクチャ探索	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	Zoph, B. & Le, Q.V.	Chen, T. & Guestrin, C.
種類	Automated architecture optimization (deep learning)	Ensemble (gradient-boosted decision trees)
原典	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
別名	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	XGBoost, extreme gradient boosting, scalable tree boosting
関連	5	5
概要	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.	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.