English(EN) Zero-Shot Neural Network Evaluation with Sample-Wise Activation Patterns

新的SWAP-Score指标可在无需训练的情况下评估神经网络

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 07:33

研究人员推出了一种新颖的零样本指标SWAP-Score，该指标无需训练即可评估神经网络。该方法使用样本激活模式来衡量网络的表达能力，并在包括CNN和Transformer在内的各种架构中表现出强大的预测性能。SWAP-Score在计算机视觉和自然语言处理任务中显著优于现有指标，与真实性能高度相关，并能实现更快的神经架构搜索。 AI

影响通过减少模型评估中的计算开销，实现更快、更准确的神经架构搜索。

排序理由该集群包含一篇介绍神经网络评估新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇介绍神经网络评估新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 109 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xiaojun Chang · 2026-05-08 07:33

基于样本激活模式的零样本神经网络评估

Zero-shot proxies, also known as training-free metrics, are widely adopted to reduce the computational overhead in neural network evaluation for scenarios such as Neural Architecture Search (NAS), as they do not require any training. Existing zero-shot metrics have several limita…

本文如何被排名

报道来源 [1]

基于样本激活模式的零样本神经网络评估

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