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Certification Hard for Transformers and Circuits

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了验证神经网络（尤其是 Transformers 和电路）精确行为的难度，即使在参数过剩极少的情况下也是如此。研究表明，即使给阈值电路增加一个额外的门，也可能使所需的验证证书大小呈指数级增长。对于对数精度 Transformers 也显示出类似的硬度结果，这表明为这些模型提供精确性保证是一个计算上具有挑战性的问题。 AI

影响 证明了验证神经网络行为的理论局限性，可能影响可靠的 AI 系统的开发。

排序理由 该集群包含一篇学术论文，详细介绍了验证神经网络行为的理论硬度结果。[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 →

Certification Hard for Transformers and Circuits

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该集群包含一篇学术论文，详细介绍了验证神经网络行为的理论硬度结果。[lever_c_demoted from research: ic=1 ai=1.0]
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134 days old
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Artur Back de Luca, Kimon Fountoulakis ·

    在最小过参数化下，示例认证对电路和Transformer模型来说很难

    arXiv:2605.22964v1 Announce Type: new Abstract: As state-of-the-art neural networks are deployed on reasoning and algorithmic tasks, exactness guarantees become increasingly important. However, high average-case accuracy can still mask inconsistent behaviors. This motivates exact…

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