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English(EN) HyperTransfer: Understanding the Equivalence between Base Optimizer and Hyperball

新框架展示神经网络优化器之间的等价性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员提出了HyperTransfer，一个新颖的框架，证明了在神经网络优化中Hyperball和基础优化器之间的动态等价性。这种等价性使得HyperTransfer仅通过目标基础优化器的初始化和学习率计划就能准确地重现其动态，而无需运行目标优化器本身。该框架已扩展到非尺度不变网络，实验表明HyperTransfer及其逆映射产生的损失轨迹几乎与其目标优化器相同。 AI

影响 引入了一种理解和潜在改进神经网络优化动态的新方法。

排序理由 学术论文，详细介绍了神经网络的新优化框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架展示神经网络优化器之间的等价性

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学术论文，详细介绍了神经网络的新优化框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jinghui Yuan, Hongtao Zhang, Jade Zou, Tianyu Li, Wenjie Zhou, Tianyu He, Wei Chen ·

    HyperTransfer：理解基础优化器与Hyperball的等价性

    arXiv:2609.07017v1 Announce Type: new Abstract: Hyperball optimizers constrain parameter norms and update only their directions, establishing a distinct paradigm for neural network optimization. Although this geometry appears fundamentally different from that of conventional Base…

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