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Symmetry-aware learning offers polynomial advantage in multi-index models

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为多指标模型的对称感知和对称不可知特征学习之间建立了多项式样本复杂度分离。他们的分析侧重于具有循环对称性的高维高斯协变量，揭示了利用对称性的架构权重共享和数据增强比不利用对称性的学习具有显著更高的样本效率。这种优势在信息指数为三或更高的多项式链接中尤为明显，其中对称感知方法在方向恢复方面需要显著更少的样本。 AI

影响 这项研究通过更好地利用固有的数据对称性，有可能带来更具样本效率的AI模型。

排序理由 该项目是一篇详细介绍特征学习理论研究的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Symmetry-aware learning offers polynomial advantage in multi-index models

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该项目是一篇详细介绍特征学习理论研究的学术论文。[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) · Jivan Waber, Vanessa Piccolo, Yatin Dandi, Florent Krzakala ·

    对称感知特征学习：多指标模型的多项式分离

    arXiv:2610.08420v1 Announce Type: new Abstract: We establish a polynomial sample complexity separation between symmetry-aware and symmetry-agnostic feature learning. We study growing-rank multi-index models with high-dimensional Gaussian covariates in $\mathbb{R}^d$ and $r=\Theta…

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