Krahasoni metodat

	Kërkimi i Arkitekturës Neurale	XGBoost
Fusha	Mësimi i thellë	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	2017	2016
Krijuesi	Zoph, B. & Le, Q.V.	Chen, T. & Guestrin, C.
Lloji	Automated architecture optimization (deep learning)	Ensemble (gradient-boosted decision trees)
Burimi themelues	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.
Emërtime të tjera	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	XGBoost, extreme gradient boosting, scalable tree boosting
Të lidhura	5	5
Përmbledhja	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.