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English(EN) Approximation of solutions of parameter-dependent problems by residual neural networks

新的神经网络训练方案利用梯度流和Lojasiewicz理论

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了一种新的神经网络训练方案，该方案利用解析激活函数并基于梯度流。该方法通过Lojasiewicz理论保证收敛性，通过求解常微分方程来近似网络系数，从而简化了实现。该方法已在参数化问题上进行了测试，成功地重现了常微分方程解对参数的依赖性，并合理地近似了具有波动约束的逆问题的解，即使在病态区域也是如此。 AI

影响 这项研究引入了一种新颖、更简单的神经网络训练方法，有望改善其在复杂参数化和逆问题中的应用。

排序理由 该集群包含一篇详细介绍新神经网络训练方法的学术论文。

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 →

新的神经网络训练方案利用梯度流和Lojasiewicz理论

报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Ana Carpio ·

    残差神经网络近似参数依赖问题的解

    arXiv:2607.13574v1 Announce Type: cross Abstract: We develop a convergent scheme to train neural networks involving analytic activation functions based on gradient flows. Convergence properties are guaranteed by Lojasiewicz theory. The main advantage of this approach is its simpl…

  2. arXiv cs.LG TIER_1 English(EN) · Ana Carpio ·

    残差神经网络近似参数相关问题的解

    We develop a convergent scheme to train neural networks involving analytic activation functions based on gradient flows. Convergence properties are guaranteed by Lojasiewicz theory. The main advantage of this approach is its simplicity of implementation. The coefficients of the n…

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

    Approximation of solutions of parameter-dependent problems by residual neural networks

    We develop a convergent scheme to train neural networks involving analytic activation functions based on gradient flows. Convergence properties are guaranteed by Lojasiewicz theory. The main advantage of this approach is its simplicity of implementation. The coefficients of the n…

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