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SGD Provably Learns Spurious Features First in Neural Networks

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新发表在arXiv上的理论研究，探讨了随机梯度下降（SGD）在两层ReLU神经网络中学习虚假特征（spurious features）的机制。研究表明，SGD会优先并以指数级的速度学习这些虚假相关性，甚至在学习实际信号之前。该研究的分析揭示，优化动态可能会将虚假特征和信号特征耦合起来，从而可能阻碍真实信号的学习，尤其是在虚假相关性很强的情况下。 AI

影响 这项研究为理解神经网络如何学习虚假相关性提供了理论基础，可能有助于开发更鲁棒的训练算法。

排序理由 该聚类包含一篇详细介绍神经网络训练动态理论发现的研究论文。

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SGD Provably Learns Spurious Features First in Neural Networks

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Tyler LaBonte, Vidya Muthukumar ·

    SGD 在 XOR 模型中可证明地优先考虑捷径虚假特征

    arXiv:2606.30444v1 Announce Type: new Abstract: Neural networks are known to be susceptible to over-reliance on spurious correlations. However, the precise mechanism by which models exploit shortcut features is not fully understood, and algorithms to mitigate this behavior rely o…

  2. arXiv stat.ML TIER_1 English(EN) · Vidya Muthukumar ·

    SGD在XOR模型中可证明地优先考虑捷径虚假特征

    Neural networks are known to be susceptible to over-reliance on spurious correlations. However, the precise mechanism by which models exploit shortcut features is not fully understood, and algorithms to mitigate this behavior rely on as yet unjustified assumptions about the learn…

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