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English(EN) Blog: Survey of Optimizers

调查显示神经网络优化器已超越Adam变体

一项对2025-2026年神经网络优化技术的最新调查显示,该领域已远远超出了简单的Adam变体。目前的研究探索了在矩阵和层上运行、适应动态训练范围以及管理分片和低精度计算的状态表示的优化器。虽然矩阵感知方法显示出潜力,但调查得出结论,AdamW仍然是一个稳健的选择,优化器的排名因模型规模、数据与参数比率以及其他因素而异。这表明优化器设计需要一种组合式方法,并需要更严格的评估协议。 AI

影响 建议采用更复杂、组合式的方法来设计和评估神经网络优化器,超越简单的变体。

排序理由 该条目是一篇关于机器学习优化技术的调查论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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调查显示神经网络优化器已超越Adam变体

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该条目是一篇关于机器学习优化技术的调查论文。[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) · Ruoran Xu ·

    博客:优化器调查

    arXiv:2608.28557v1 Announce Type: new Abstract: Neural-network optimization in 2025-2026 is no longer well described as a succession of new Adam variants. The design space has expanded from coordinates to matrices and layers, from fixed training horizons to policies over time, an…