Neural architecture search (NAS) is the automated design of a neural network’s structure, the number and type of layers, how they connect, and the operations inside them, by treating that design as a search or optimization problem rather than a task for human intuition. It is the deep-learning-specific branch of AutoML.
The concept was popularized by the 2016 paper “Neural Architecture Search with Reinforcement Learning” by Zoph and Le, which trained a controller network to propose architectures and rewarded it for the accuracy of the resulting models. Because evaluating each candidate meant training a network, early NAS was extremely expensive, which drove a wave of efficiency research: weight-sharing approaches that reuse computation across candidates, and differentiable methods such as DARTS that relax the discrete search into a continuous one solvable by gradient descent. Reproducibility benchmarks like NAS-Bench-101 later let researchers compare methods fairly and cheaply. NAS has produced competitive architectures in vision and language and fed into products such as Google’s image models.
For a business reader, NAS represents the field automating one of its most specialized crafts, with the practical caveat that the search itself can consume significant compute.