Neural Architecture Search (NAS)

What is NAS? Automated process of discovering optimal neural network architectures for given tasks, replacing manual architecture design.

NAS Components

Search Space Define what architectures are possible:

search_space = {
    "num_layers": [4, 6, 8, 12],
    "hidden_size": [256, 512, 768, 1024],
    "num_heads": [4, 8, 12],
    "activation": ["relu", "gelu", "swish"],
    "dropout": [0.0, 0.1, 0.2]
}

Search Strategy

Performance Estimation

DARTS (Differentiable Architecture Search)

# Continuous relaxation of architecture choice
alpha = nn.Parameter(torch.randn(num_ops))  # Architecture weights

def forward(x):
    ops_outputs = [op(x) for op in operations]
    weights = F.softmax(alpha, dim=0)
    return sum(w * o for w, o in zip(weights, ops_outputs))

# After training, select highest-weight operations
final_arch = alpha.argmax(dim=0)

AutoML Platforms

Use Cases

• Find efficient architectures for deployment
• Discover architectures for new domains
• Optimize for specific hardware constraints
• Automate ML pipeline development

Best Practices

• Define search space based on domain knowledge
• Use early stopping for efficiency
• Validate on held-out data
• Consider transfer from similar tasks