Neural architecture search

Machine learning and data mining
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Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par with or outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used:

The search space defines the type(s) of ANN that can be designed and optimized.
The search strategy defines the approach used to explore the search space.
The performance estimation strategy evaluates the performance of a possible ANN from its design (without constructing and training it).

NAS is closely related to hyperparameter optimization and meta-learning and is a subfield of automated machine learning (AutoML).