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New methods accelerate Neural Architecture Search with reduced computational cost · 3 sources tracked

By PulseAugur Editorial · [4 sources] ·

Researchers have developed new methods to improve Neural Architecture Search (NAS), a process that can be computationally expensive. One approach, RiPPLE, uses partial training data from a small set of anchor architectures to predict the performance of many others, reducing the need for full training. Another method, CoRA-NAS, combines a coarse ranking of architectures with a refinement stage that extrapolates early training curves to estimate performance with significantly lower cost. A third technique addresses the expense of Leave-One-Subject-Out (LOSO) evaluation in NAS by proposing a leakage-free, block-based approach that shares computation across subjects, drastically reducing resource requirements while maintaining accuracy. AI

IMPACT These methods aim to significantly reduce the computational cost of finding optimal neural network architectures, potentially accelerating AI development and deployment.

RANK_REASON The cluster contains multiple academic papers introducing new methods for neural architecture search.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New methods accelerate Neural Architecture Search with reduced computational cost · 3 sources tracked

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The cluster contains multiple academic papers introducing new methods for neural architecture search.
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4 independent sources
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paper, infra
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COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Yifan Yang, Zhaoyan Wang, Zheng Gao, Xiaoyu Li, Jiaojiao Jiang ·

    RiPPLE: Cross-Space Performance Prediction from Early Training for Neural Architecture Search

    arXiv:2609.12418v1 Announce Type: new Abstract: Neural architecture search (NAS) evaluates candidate networks, but fully training enough architectures to rank an entire space is expensive. Zero-cost proxies score architectures at initialization, yet their ranking quality varies a…

  2. arXiv cs.AI TIER_1 English(EN) · Heinke Hihn ·

    Efficient Leakage-Free Neural Architecture Search under Leave-One-Subject-Out Evaluation

    arXiv:2609.09433v1 Announce Type: cross Abstract: Leave-One-Subject-Out (LOSO) evaluation estimates generalisation performance for subject-based classification but makes Neural Architecture Search (NAS) computationally expensive because a fully nested implementation requires N in…

  3. arXiv cs.LG TIER_1 English(EN) · Yifan Yang, Zhaoyan Wang, Zheng Gao, Xiaoyu Li, Jiaojiao Jiang ·

    CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

    arXiv:2609.11884v1 Announce Type: new Abstract: Zero-cost proxies rank architectures cheaply, but their reliability varies across search spaces. We introduce CoRA-NAS (COarse Ranking + Anchor-residual), a two-stage framework combining a static ranking prior with low-cost learning…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

    Zero-cost proxies rank architectures cheaply, but their reliability varies across search spaces. We introduce CoRA-NAS (COarse Ranking + Anchor-residual), a two-stage framework combining a static ranking prior with low-cost learning-curve refinement. CoRA-Rank aggregates capacity…

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