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English(EN) Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism

AI验证通过新的并行技术进行扩展

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员已将通常用于训练大型模型的张量并行和全分片数据并行技术应用于改进神经网络验证的可扩展性。这些方法解决了以前限制形式验证算法的GPU内存限制。研究表明，内存显著减少，其中FSDP在保持与单GPU系统位相同的界限的同时，基线内存下降高达90%。 AI

影响 能够验证更大、更复杂的神经网络，这对于安全关键的AI应用至关重要。

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 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

AI验证通过新的并行技术进行扩展

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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Sergei Vorobyov, Eugene Ilyushin ·

    使用张量并行和全分片数据并行扩展神经网络验证

    arXiv:2606.09377v1 Announce Type: cross Abstract: Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms…

  2. arXiv cs.LG TIER_1 English(EN) · Eugene Ilyushin ·

    使用张量并行和完全分片数据并行扩展神经网络验证

    Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $α$-CROWN) require weight and relaxa…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    使用张量并行和全分片数据并行扩展神经网络验证

    Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $α$-CROWN) require weight and relaxa…

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