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English(EN) Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model

神经网络在学习高斯单指标模型中实现最优权衡

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的基于梯度的算法来训练两层神经网络，该算法可以在学习高斯单指标模型时实现最优的计算-统计权衡。这种新方法在所有生成指数上都匹配了统计查询（SQ）下界（在多对数因子内），解决了机器学习中一个长期存在的问题。该算法能够适应各种损失函数和激活函数，并引入了一种新的权重扰动技术用于稀疏设置，这表明其在稀疏张量PCA等领域具有更广泛的应用前景。 AI

影响 这项研究推进了对神经网络能力的理论理解，并可能带来更高效的复杂模型学习算法。

排序理由 该集群包含一篇详细介绍机器学习新算法和理论分析的学术论文。

在 arXiv cs.LG 阅读 →

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神经网络在学习高斯单指标模型中实现最优权衡

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该集群包含一篇详细介绍机器学习新算法和理论分析的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Siyu Chen, Beining Wu, Miao Lu, Zhuoran Yang, Tianhao Wang ·

    神经网络能否实现最优计算-统计权衡？单指标模型分析

    arXiv:2606.15219v1 Announce Type: new Abstract: In this work, we tackle the following question: Can neural networks trained with gradient-based methods achieve the optimal computational-statistical tradeoff in learning Gaussian single-index models? Prior research has shown that a…

  2. arXiv stat.ML TIER_1 English(EN) · Tianhao Wang ·

    神经网络能否实现最优计算-统计权衡？单指标模型分析

    In this work, we tackle the following question: Can neural networks trained with gradient-based methods achieve the optimal computational-statistical tradeoff in learning Gaussian single-index models? Prior research has shown that any polynomial-time algorithm under the statistic…

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