Neural Architecture Search (NAS)

Neural Architecture Search is an automated process that uses machine learning to discover optimal neural network architectures for specific tasks, essentially designing AI systems that can build other AI systems.

Neural Architecture Search works by systematically exploring different neural network designs to find the most effective architecture for a given problem. The process involves defining a search space of possible architectures, using a search strategy to explore this space, and evaluating the performance of candidate architectures.

This technology represents a significant advancement in AutoML (Automated Machine Learning), as it removes much of the human trial and error from neural network design. NAS can discover architectures that match or exceed human-designed networks, though it often requires substantial computational resources to achieve these results.

Examples

• Mobile network architecture optimisation
• Computer vision model design
• Speech recognition system development
• Natural language processing architectures
• Efficient edge device neural networks