In Depth
Neural Architecture Search (NAS) automates the process of designing neural network architectures. Instead of a human researcher manually deciding how many layers, what type of connections, and what activation functions to use, NAS algorithms explore the design space systematically to find architectures that maximize performance for a given task and compute budget.
Early NAS methods used reinforcement learning or evolutionary algorithms to search over architectures, but these were extremely computationally expensive (thousands of GPU-hours). Modern approaches like DARTS (Differentiable Architecture Search) and one-shot methods dramatically reduce the search cost by making the architecture search differentiable or by training a single super-network that contains all candidate architectures.
NAS has produced architectures that outperform human-designed ones in several domains, including EfficientNet for image classification and NASNet for object detection. For businesses, NAS is increasingly accessible through AutoML platforms that automatically find optimal architectures for specific datasets and constraints, democratizing model design beyond specialized AI researchers.