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English(EN) A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm

新的神经网络提前停止规则可绕过训练

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的、依赖数据的神经网络训练提前停止规则，该规则可以解析地估计泛化误差，从而无需通过梯度下降进行数值估计。这个新颖的框架基于具有L1范数的Rademacher复杂度，为依赖随机矩阵理论且通常对数据分布做出限制性假设的现有方法提供了一种替代方案。该方法最初专注于线性模型，但通过线性探测可以扩展到非线性神经网络，如在MNIST分类示例中所示。 AI

影响 这项研究通过优化提前停止过程，可能导致更有效的神经网络训练，从而降低计算成本并提高泛化能力。

排序理由 该集群描述了一篇新发表在arXiv上的研究论文，其中详细介绍了一种新颖的神经网络训练方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的神经网络提前停止规则可绕过训练

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该集群描述了一篇新发表在arXiv上的研究论文，其中详细介绍了一种新颖的神经网络训练方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Duy Hoang, Bastien Berret, Olivier Bruneau, Laurent Fribourg ·

    一种基于Rademacher复杂度与L1范数的依赖数据的早期停止规则

    arXiv:2608.24210v1 Announce Type: new Abstract: Training neural networks requires balancing the trade-off between fitting the training data and achieving robust performance on unseen inputs. This ability, commonly referred to as generalizability, is determined by the gap between …

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

    一种基于Rademacher复杂度与L1范数的依赖数据的早期停止规则

    Training neural networks requires balancing the trade-off between fitting the training data and achieving robust performance on unseen inputs. This ability, commonly referred to as generalizability, is determined by the gap between the empirical risk on the training set (``empiri…

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