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ES-HyperNEAT 超参数优化在 MNIST 上显示出可迁移性

研究人员使用基于树的 Parzen 估计器 (TPE) 方法研究了 ES-HyperNEAT 算法的超参数优化。将其应用于 MNIST 分类任务,TPE 成功导航了超过 30 亿种潜在组合,实现了 29.00% 的准确率,并且比之前的研究使用了更少的代数和更小的种群规模。优化后的超参数在 Fashion-MNIST 数据集上表现出可迁移性,尽管它们在更简单的逻辑运算上的有效性有限。 AI

影响 为神经进化算法的优化提供了一种方法,并深入了解了跨任务的超参数可迁移性。

排序理由 一篇发表在 arXiv 上的学术论文,详细介绍了一种针对神经进化算法的超参数优化新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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ES-HyperNEAT 超参数优化在 MNIST 上显示出可迁移性

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一篇发表在 arXiv 上的学术论文,详细介绍了一种针对神经进化算法的超参数优化新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Kilian Stoffel ·

    Investigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach

    Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-HyperNEAT), have shown great potential in developing neural networks. However, their effectiveness heavily depends on the selection of hyperparameters. This study investigat…