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English(EN) Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses

新的自适应Adam优化器提高了深度学习求解偏微分方程的收敛性

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文介绍了一种学习率自适应的Adam优化器变体，旨在提高深度学习的收敛性，特别是在求解偏微分方程方面。所提出的方法根据目标函数的经验估计来调整学习率，旨在克服标准Adam和具有恒定学习率的SGD的局限性。数值模拟表明，与默认的Adam优化器相比，目标函数值减少得更快，并且理论分析为某些自适应SGD变体收敛到全局最小值的严格证明提供了依据。 AI

影响 这项研究可能导致深度学习模型（尤其是在复杂的科学模拟中）的训练更加稳定和高效。

排序理由 该集群包含一篇详细介绍深度学习新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的自适应Adam优化器提高了深度学习求解偏微分方程的收敛性

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该集群包含一篇详细介绍深度学习新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Steffen Dereich, Arnulf Jentzen, Adrian Riekert ·

    学习率自适应随机梯度下降优化方法：用于偏微分方程的深度学习方法的数值模拟与收敛性分析

    arXiv:2406.14340v2 Announce Type: replace-cross Abstract: The standard stochastic gradient descent (SGD) optimization method, as well as adaptive methods such as the Adam optimizer fail to converge if the learning rates do not converge to zero (particularly, in the situation of c…

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