Neural architecture search
Machine learning-powered structure design / From Wikipedia, the free encyclopedia
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Neural architecture search (NAS)[1][2] 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 or outperform hand-designed architectures.[3][4] Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used:[1]
- 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[5] and meta-learning[6] and is a subfield of automated machine learning (AutoML).[7]