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Computer Vision Lab, CAIDAS
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LLM-based neural architecture search isn't just trial and error: this paper proves it converges, and derives a surprisingly simple formula to check if your proxy metrics are trustworthy.
LLMs can now generate neural architectures with 75% less code and higher accuracy by learning to write code "diffs" instead of building from scratch.
Forget cloud TPUs: this NAS method coaxes surprisingly good CNN architectures out of commodity GPUs using LLMs and a clever feedback loop.
LLMs can evolve into autonomous neural architecture designers, learning to generate novel and high-performing architectures by internalizing execution feedback, even surpassing their initial training data.
