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New LESS method slashes neural architecture search time to minutes

Researchers have developed LESS (Lightweight Evolutionary Supernet Search), a novel method for neural architecture search that significantly reduces computation time. By combining a brief warm-up with a discrete search under a CMA-ES distribution, LESS achieves competitive accuracy on benchmarks like CIFAR-10 in minutes, a fraction of the time required by previous methods. The approach demonstrates effectiveness across different datasets and search spaces, including the larger DARTS space, highlighting its efficiency and broad applicability. AI

IMPACT Enables faster and more efficient exploration of AI model architectures, potentially accelerating research and development cycles.

RANK_REASON The cluster contains an academic paper detailing a new method for neural architecture search.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New LESS method slashes neural architecture search time to minutes

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The cluster contains an academic paper detailing a new method for neural architecture search.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Aviral Gandhi, Jinglue Xu, Jialong Li, Hitoshi Iba ·

    LESS: Lightweight Evolutionary Supernet Search in Minutes

    arXiv:2610.01468v1 Announce Type: cross Abstract: Low-cost NAS must both explore high-performing architectures and identify them reliably, yet reducing evaluation cost often weakens the fidelity of candidate comparisons. Training-free methods reduce evaluation cost by replacing l…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Hitoshi Iba ·

    LESS: Lightweight Evolutionary Supernet Search in Minutes

    Low-cost NAS must both explore high-performing architectures and identify them reliably, yet reducing evaluation cost often weakens the fidelity of candidate comparisons. Training-free methods reduce evaluation cost by replacing learned task feedback with proxy signals measured a…