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	Bayesiansk optimering	Neural Architecture Search
Ämnesområde	Optimering	Djupinlärning
Familj	Process / pipeline	Machine learning
Ursprungsår	1975 (foundational); 2012 (ML standard)	2017
Upphovsperson	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)	Zoph, B. & Le, Q.V.
Typ	Sequential model-based black-box optimization	Automated architecture optimization (deep learning)
Ursprungskälla	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.
Alias	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Närliggande	2	5
Sammanfattning	Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones.	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.