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English(EN) ManifoldFlow: SPD-Relaxed Stiefel Layers with Learnable Singular Spectrum

ManifoldFlow为神经网络权重引入可学习的奇异谱

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员引入了ManifoldFlow，一种新颖的方法，它放宽了传统神经网络中Stiefel层的约束。这种新方法允许可学习的奇异值，为神经网络权重的谱控制提供更大的灵活性。ManifoldFlow在循环语言模型投影以及其他需要正交基的序列、表格和图像实验中，相比固定奇异谱的Stiefel层表现出改进。 AI

影响 这项研究为神经网络中的谱控制提供了一种更灵活的方法，有望提高语言模型和其他应用中的性能。

排序理由 该集群包含一篇详细介绍神经网络层新方法的学术论文。

在 arXiv stat.ML 阅读 →

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ManifoldFlow为神经网络权重引入可学习的奇异谱

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Haiwen Yi, Xinyuan Song ·

    ManifoldFlow: 具有可学习奇异谱的SPD-松弛Stiefel层

    arXiv:2607.04535v1 Announce Type: cross Abstract: Orthogonal and Stiefel layers give neural weights exact spectral control, but they also impose a strong modeling constraint: all represented singular values are fixed at one. Many settings that benefit from an orthonormal basis st…

  2. arXiv stat.ML TIER_1 English(EN) · Xinyuan Song ·

    ManifoldFlow: 具有可学习奇异谱的SPD松弛Stiefel层

    Orthogonal and Stiefel layers give neural weights exact spectral control, but they also impose a strong modeling constraint: all represented singular values are fixed at one. Many settings that benefit from an orthonormal basis still need direction-dependent attenuation or amplif…

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