LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.
Journal of machine learning research13(2) (2012)
2 Pith papers cite this work. Polarity classification is still indexing.
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Static code metrics yield near-zero predictive power for Java method energy; adding execution time raises R² to 0.46, showing energy is dominated by runtime behavior.
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LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search
LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.
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Static Metrics Are Insufficient: Predicting Java Method Energy Usage with Execution Time
Static code metrics yield near-zero predictive power for Java method energy; adding execution time raises R² to 0.46, showing energy is dominated by runtime behavior.