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English(EN) Shifting-based Optimizable Linear Relaxations for General Activation Functions

新的SLiR方法通过广泛的适用性增强了神经网络验证

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为SLiR（基于偏移的线性松弛）的新方法，用于验证神经网络的行为。该方法广泛适用于各种激活函数，仅需要Lipschitz常数或关键点。SLiR通过斜率参数化松弛，并计算偏移量以确保可靠的界限，从而实现高效且正确的优化。实验表明，SLiR产生的松弛界限紧密，并且与现有方法相比，能够验证更多的属性。 AI

影响 增强了对神经网络行为进行形式化验证的能力，有可能提高关键应用的安全性与可靠性。

排序理由 该集群包含一篇详细介绍神经网络验证新方法的学术论文。

在 arXiv cs.LG 阅读 →

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新的SLiR方法通过广泛的适用性增强了神经网络验证

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Philipp Kern, L\'aszl\'o Antal, Erika \'Abr\'aham, Carsten Sinz ·

    面向通用激活函数的基于移位和可优化的线性松弛

    arXiv:2606.20292v1 Announce Type: new Abstract: The use of neural networks (NNs) is rapidly increasing, including in safety- and security-critical domains. To provide formal guarantees about NN behavior, many verification methods rely on optimizable linear relaxations of activati…

  2. arXiv cs.LG TIER_1 English(EN) · Carsten Sinz ·

    面向通用激活函数的基于移位和可优化的线性松弛

    The use of neural networks (NNs) is rapidly increasing, including in safety- and security-critical domains. To provide formal guarantees about NN behavior, many verification methods rely on optimizable linear relaxations of activation functions. However, existing techniques depen…

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