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English(EN) Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures

新研究系统性评估了不同神经网络架构的学习率调度器

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新发表在arXiv上的研究论文详细介绍了对神经网络学习率调度策略的系统性评估。该研究将25种调度器配置应用于3,938个模型变体，使用了来自卷积和Transformer家族的30种不同架构。研究结果表明，最优调度器高度依赖于特定架构，其中CosineAnnealingWarmRestarts和CyclicLR的表现优于简单的衰减方法。该研究为LEMUR神经网络数据集贡献了一个全面的准确性图谱，为选择合适的调度器提供了实用指南。 AI

影响 为选择最优学习率调度器提供了实用参考，有望提高各种神经网络架构的训练效率和准确性。

排序理由 该集群包含一篇研究论文，详细介绍了对神经网络学习率调度策略的系统性评估。

在 arXiv cs.LG 阅读 →

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新研究系统性评估了不同神经网络架构的学习率调度器

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该集群包含一篇研究论文，详细介绍了对神经网络学习率调度策略的系统性评估。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hafsa Mateen, Radu Timofte, Dmitry Ignatov ·

    异构架构下学习率调度策略的系统性评估

    arXiv:2607.08511v1 Announce Type: new Abstract: Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive. While classical AutoML approaches often treat the scheduler as a secondary hyperparameter, we…

  2. arXiv cs.LG TIER_1 English(EN) · Dmitry Ignatov ·

    异构架构下学习率调度策略的系统性评估

    Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive. While classical AutoML approaches often treat the scheduler as a secondary hyperparameter, we systematically investigate its impact on classi…

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