Võrdle meetodeid

	Bayesi optimeerimine – järjestikune mudelipõhine hüperparameetrite häälestamine	Neuraalarhitektuuri otsing
Valdkond	Optimeerimine	Süvaõpe
Perekond	Process / pipeline	Machine learning
Tekkeaasta	1975 (foundational); 2012 (ML standard)	2017
Looja	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)	Zoph, B. & Le, Q.V.
Tüüp	Sequential model-based black-box optimization	Automated architecture optimization (deep learning)
Algallikas	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.
Rööpnimetused	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Seotud	2	5
Kokkuvõte	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.