19.3 Neural architecture search and AutoML

Neural Architecture Search (NAS) revolutionizes deep learning by automating network design. It reduces human involvement, enabling discovery of novel architectures like ResNet and EfficientNet. NAS adapts to specific tasks and datasets, making it versatile for various applications.

NAS algorithms use reinforcement learning or evolutionary approaches to explore architecture spaces. Popular AutoML frameworks like Google AutoML and Auto-Keras implement NAS. Evaluation involves benchmarking on diverse datasets, considering metrics like accuracy, inference time, and model size.

Neural Architecture Search and AutoML Fundamentals

Concept of neural architecture search

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• Neural Architecture Search (NAS) automates optimal neural network architecture design reducing human involvement
• NAS components include search space (possible architectures), search strategy (exploration method), and performance estimation strategy (candidate evaluation)
• NAS reduces reliance on domain expertise enabling discovery of novel architectures (ResNet, EfficientNet) and adapts to specific tasks and datasets (image classification, natural language processing)

Implementation of NAS algorithms

• Reinforcement Learning-based NAS uses controller network to generate architecture descriptions with reward signal based on validation performance
• Evolutionary NAS employs population of candidate architectures with genetic operators (mutation, crossover) and selection based on fitness metrics
• Popular AutoML frameworks include Google AutoML, Microsoft NNI, and Auto-Keras
• Implementation steps:
  1. Define search space and constraints
  2. Choose search algorithm (RL or evolutionary)
  3. Set up performance evaluation pipeline
  4. Configure hyperparameters for search process

Evaluation and Future Directions

Performance of automated architectures

• Benchmark datasets for evaluation span image classification (CIFAR-10, ImageNet), natural language processing (GLUE, SQuAD), and speech recognition (LibriSpeech, CommonVoice)
• Evaluation metrics encompass accuracy, precision, recall, F1-score, inference time, model size, and FLOPs
• Comparison methodology involves training NAS-generated and manually designed models using consistent protocols and performing statistical significance tests
• Analysis of trade-offs considers performance vs computational cost and generalization ability across tasks

Challenges in NAS and AutoML

• Computational cost of architecture search remains a significant challenge
• Search space design incorporates domain knowledge, hierarchical structures, and multi-objective optimization for conflicting goals
• Improving computational efficiency through weight sharing techniques, progressive neural architecture search, and one-shot NAS methods
• Transferability of learned architectures explored through cross-domain adaptation, few-shot architecture search, and meta-learning
• Future directions include automated feature engineering, joint optimization of architectures and training strategies, and hardware-aware design integration
• Explainable AutoML aims for interpretable model selection enhancing transparency and trust in automated systems