What is NAS?
Neural Architecture Search (NAS) automates the design of deep learning models, replacing manual design with algorithms that search through millions of possible network configurations.
The Traditional Approach
Manual neural network design requires extensive domain expertise and iterative experimentation. This process is time-consuming and often suboptimal for modern hardware constraints.
Key Innovations
Reinforcement Learning
Using RL agents to explore the search space efficiently while maintaining diversity in discovered architectures.
Differentiable Search
Gradient-based methods that enable continuous relaxation of discrete architecture choices for faster optimization.