So sánh phương pháp

	Tìm kiếm Kiến trúc Mạng Nơ-ron	XGBoost
Lĩnh vực	Học sâu	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2017	2016
Người khởi xướng	Zoph, B. & Le, Q.V.	Chen, T. & Guestrin, C.
Loại	Automated architecture optimization (deep learning)	Ensemble (gradient-boosted decision trees)
Công trình gốc	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.
Tên gọi khác	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	XGBoost, extreme gradient boosting, scalable tree boosting
Liên quan	5	5
Tóm tắt	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.