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LLM-guided framework enhances neural architecture search proxies

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Bi-EZP, a novel bilevel framework designed to improve the discovery of ensemble zero-cost proxies for neural architecture search (NAS). This framework separates the discrete structural optimization of aggregation programs from the continuous calibration of their parameters. A large language model generates executable aggregation programs, which are then optimized using covariance matrix adaptation evolution strategy (CMA-ES). The effectiveness of these programs is evaluated on a separate validation split, allowing the evolutionary process to favor structures that generalize well. AI

IMPACT This research could lead to more efficient and effective neural architecture search, potentially accelerating the development of new AI models.

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.AI →

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

LLM-guided framework enhances neural architecture search proxies

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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.AI TIER_1 English(EN) · Yutao Lai, Kezhao Lai, Hai-Lin Liu ·

    Bi-EZP: LLM-Guided Bilevel Program Evolution for Ensemble Zero-Cost Proxy Discovery

    arXiv:2608.21927v1 Announce Type: cross Abstract: Zero-cost proxies enable neural architecture search (NAS) to rank candidate networks from statistics computed at initialization, avoiding repeated training. However, different proxies capture different properties and often produce…

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