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English(EN) TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks

新的TAO协议验证浮点神经网络

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为TAO（容差感知乐观验证）的新验证协议，旨在确保浮点神经网络计算的完整性，特别是在云端机器学习服务中。TAO通过接受原则性接受区域内的输出来解决不同硬件上非确定性浮点执行的挑战，而不是要求逐位相等。该系统结合了理论最坏情况界限和经验百分位数剖面，并使用争议博弈将差异递归地缩小到单个运算符，从而使验证对于实际的机器学习模型具有可扩展性和实用性。 AI

影响 通过提供一种可验证的方法来确保模型计算的完整性，增强了对机器学习服务的信任。

排序理由 详细介绍神经网络新验证协议的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新的TAO协议验证浮点神经网络

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详细介绍神经网络新验证协议的学术论文。[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) · Jianzhu Yao, Hongxu Su, Taobo Liao, Zerui Cheng, Huan Zhang, Xuechao Wang, Pramod Viswanath ·

    TAO：浮点数神经网络的容差感知乐观验证

    arXiv:2510.16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little about what actually ran or whether returned outputs faithfully reflect the in…

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