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

作者 PulseAugur 编辑部 · [2 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。他们对具有循环对称性的高维高斯协变量的分析表明，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，对称感知方法在方向恢复方面需要的样本量大大减少。 AI

影响 这项研究可能通过更好地利用固有的数据对称性，从而开发出更具样本效率的AI模型。

排序理由 该条目是一篇学术论文，详细介绍了特征学习的理论研究。[lever_c_demoted from research: ic=1 ai=1.0]

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

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该条目是一篇学术论文，详细介绍了特征学习的理论研究。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  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…

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

    对称感知特征学习：多指标模型的多项式分离

    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 $r=Θ(d^δ)$ teacher directions forming a cyclic symmetry o…

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