Three-example few-shot prompting optimizes LLM-generated vision architectures while a whitespace-normalized hash provides 100x faster duplicate detection than AST parsing across seven benchmarks.
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LEMUR 2 releases a multi-generator, multi-task neural-architecture corpus with real-device latency metadata intended as fuel for LLM-driven AutoML.
FractalNet automatically generates and tests over 1,200 CNN architectures based on recursive fractal templates, achieving up to 80.18% accuracy on CIFAR-10 after five training epochs.
citing papers explorer
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Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design
Three-example few-shot prompting optimizes LLM-generated vision architectures while a whitespace-normalized hash provides 100x faster duplicate detection than AST parsing across seven benchmarks.
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LEMUR 2: Unlocking Neural Network Diversity for AI
LEMUR 2 releases a multi-generator, multi-task neural-architecture corpus with real-device latency metadata intended as fuel for LLM-driven AutoML.
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Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis
FractalNet automatically generates and tests over 1,200 CNN architectures based on recursive fractal templates, achieving up to 80.18% accuracy on CIFAR-10 after five training epochs.