Automating Architecture Design
Neural Architecture Search (NAS) automates the design of neural network architectures, using algorithms to discover optimal structures that often outperform hand-crafted designs.
Key Components:
• Search Space - Possible architectures to explore
• Search Strategy - How to explore the space
• Performance Estimation - Evaluate architectures efficiently
• Hardware Constraints - Memory, latency requirements
🚀 Why Use NAS?
Discovers novel architectures that achieve better accuracy-efficiency trade-offs than human designs, especially for specific datasets and hardware constraints.
NAS Approaches
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🎮
Reinforcement Learning NAS
Controller network learns to design architectures
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🧬
Evolutionary NAS
Evolve populations of architectures over generations
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📈
Differentiable NAS (DARTS)
Gradient-based optimization of architecture parameters
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⚡
One-Shot NAS
Train supernet once, then search efficiently