新的反例挑战了单神经元AI训练中的傅里叶对齐

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-05 04:56

研究人员提出了一个单神经元模加傅里叶对齐假设的反例。该反例表明，一个ReLU神经元可能会变得不活跃，并保持在极限状态，其傅里叶能量分布在所有非零实频率上。在高斯初始化下，这种失败以正概率发生，并且对于Clarke轨迹和ReLU的光滑近似，使用固定步长梯度下降也可能发生。这一发现表明，在训练单个神经元进行模加时，单频率对齐并非必然结果。 AI

影响挑战了关于神经网络训练动态和单神经元操作潜在故障模式的假设。

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-08-05 04:56

单神经元模加的傅里叶对齐反例

We give a negative solution to MAIS-O60. We first construct an example in which an initially active ReLU neuron becomes completely inactive in finite time and thereafter remains frozen at a limit whose Fourier energy is equally distributed among all nonzero real frequency classes…

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单神经元模加的傅里叶对齐反例

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