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English(EN) Certified Inference and Training for Deep Equilibrium Networks: A Continuation Framework with Polynomial Complexity Guarantees

新框架为深度均衡网络提供可认证训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为深度均衡网络（DEQs）开发了一个新框架，该框架为推理和训练提供了可认证的保证。该框架使用连续性方法来实现多项式复杂度，确保高达 $2^{-b}$ 的精度。对于推理，它采用了紧凑输入同伦和带有可认证边界的四舍五入牛顿跟踪器。训练通过局部加低秩递归和可编程休眠通道得到增强，并利用加载的Tikhonov诊断和修复插值失败。该系统使用Lean 4编程语言进行了验证，数值比较证明了其有效性。 AI

影响 为DEQs的训练和推理引入了新颖的理论框架，有望提高复杂AI系统的可靠性和效率。

排序理由 详细介绍具有理论保证的深度均衡网络新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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

新框架为深度均衡网络提供可认证训练

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详细介绍具有理论保证的深度均衡网络新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Alex Borisevich ·

    深度均衡网络的认证推理与训练：具有多项式复杂度保证的连续框架

    arXiv:2609.16485v1 Announce Type: new Abstract: We develop a certified continuation framework for equilibrium computation and for training deep equilibrium networks (DEQs), with training formulated as interpolation to accuracy $2^{-b}$. For inference, compact input homotopy selec…

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