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LLMs structure design knowledge for efficient neural architecture search

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for neural architecture search (NAS) that leverages large language models (LLMs) to structure design knowledge, enabling more efficient exploration of open-ended search spaces. The proposed approach, FairNAD, defines a high-level structural template for architectural attributes, which an LLM populates by analyzing research papers. This creates a rich search space that guides the NAS process, leading to improved performance on benchmark datasets like CIFAR-10, CIFAR-100, and ImageNet16-120. AI

IMPACT Enhances efficiency in AI model design by structuring knowledge for automated architecture discovery.

RANK_REASON The cluster contains an academic paper detailing a new method for neural architecture search. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs structure design knowledge for efficient neural architecture search

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The cluster contains an academic paper detailing a new method for neural architecture search. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yuiko Sakuma, Masakazu Yoshimura, Marcel Gr\"opl, Zitang Sun, Junji Otsuka, Atsushi Irie, Takeshi Ohashi ·

    Structuring Open-Ended NAS: Semi-Automated Design Knowledge Structuring with LLMs for Efficient Neural Architecture Search

    arXiv:2605.19247v2 Announce Type: replace Abstract: Current neural architecture search (NAS) methods are often limited by their predefined, restrictive search spaces. While recent large language model (LLM)-assisted NAS methods enable open-ended search spaces, they often suffer f…

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