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English(EN) Sphere Retraction Normalizations

球体回缩归一化泛化深度神经网络训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了球体回缩归一化（Sphere Retraction Normalizations），一个用于训练深度神经网络的新框架，它泛化了现有的残差连接方法。该方法在黎曼流形上重塑残差连接，通过将隐藏状态限制在超球面上来确保其保持恒定的 L2 范数。该框架引入了两种新方法：Proj-SpheretNorm 和 Cay-SpheretNorm，它们利用代数运算，在 nanoGPT 上表现优于当前的轻量级深度连接方案，表明指数映射并非球形残差流的最佳回缩方式。 AI

影响 引入了一个新颖的神经网络训练框架，该框架泛化了现有方法并在基准测试中表现出改进的性能。

排序理由 介绍神经网络新归一化技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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球体回缩归一化泛化深度神经网络训练

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介绍神经网络新归一化技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jie Zhang, Cheng-Fang Su, Yi-Jui Huang, Min-Te Sun ·

    Sphere Retraction Normalizations

    arXiv:2608.02668v1 Announce Type: cross Abstract: Residual connections are the de facto mechanism for training deep neural networks stably. Geodesic Normalization (GeoNorm) recasts them on a Riemannian manifold, orthogonalizing each layer output against the current hidden state a…

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