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English(EN) Coupled Scaling: A Representational Accessibility Framework for Neural Scaling Laws

新框架“耦合缩放”重新定义神经缩放定律

研究人员引入了耦合缩放(Coupled Scaling),一个用于理解神经缩放定律的新框架,该框架考虑了架构和优化如何影响模型可访问的表征。这种任务条件方法认为,有限预算缩放受到任务结构与系统可访问几何形状之间相互作用的影响。该框架将架构支持与有限预算获取分开,并提出独立于缩放拟合来测量几何形状的测试,通过对现有涌现轨迹的审计来识别因子测试的控制因素。 AI

影响 引入了一个新的理论框架,可能改进对模型缩放行为的理解和预测。

排序理由 该集群包含一篇详细介绍神经缩放定律新理论框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新框架“耦合缩放”重新定义神经缩放定律

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该集群包含一篇详细介绍神经缩放定律新理论框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jie Wang ·

    耦合缩放:神经网络缩放定律的表征可达性框架

    arXiv:2609.03533v1 Announce Type: new Abstract: Existing theories derive neural scaling from data geometry or a specified data-model spectrum, but systems trained on the same data can scale differently when architecture or optimization changes the representations they can efficie…