What is Neural Architecture Search (NAS)?

Definition
Neural Architecture Search (NAS) automatically discovers optimal neural network architectures by searching through a defined space of possible designs, replacing manual architecture engineering with algorithmic exploration.
The Problem It Solves
Manual architecture design is slow and requires deep expertise. An expert might try 50-100 architecture variations over months, but the search space contains millions of possible designs. Human intuition cannot exhaustively explore this space.
NAS automates this exploration. It evaluates thousands of architectures systematically, finding designs that humans might never consider. The result is often architectures that outperform hand-designed ones by 1-5% accuracy at the same or lower compute cost.
Three Core Components
Search space: Defines what architectures are possible. This includes layer types (convolution, attention, pooling), connections (skip connections, dense blocks), and sizes (channel counts, depths). Larger spaces find better architectures but cost more to search.
Search strategy: How to explore the space. Options include reinforcement learning, evolutionary algorithms, or gradient-based methods. Each has different exploration-exploitation tradeoffs.
Evaluation strategy: How to score each candidate architecture. Full training is expensive (hours to days per architecture). Multi-fidelity methods train partially or use proxies to estimate final accuracy cheaply.

💡 Key Takeaways

✓NAS explores millions of architecture combinations that humans cannot manually test

✓Three components: search space, search strategy, evaluation strategy

✓Found architectures often beat hand-designed ones by 1-5% accuracy

✓Search cost is the main limitation: thousands of GPU hours possible

📌 Interview Tips

1Interview Tip: Explain why the search space definition is critical and how it constrains what NAS can find

2Interview Tip: Compare reinforcement learning vs evolutionary search strategies

3Interview Tip: Discuss the compute cost tradeoff when expanding search space

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