English(EN) Gradient Span Algorithms Make Predictable Progress in High Dimension

梯度跨度算法在高维机器学习中展现出可预测的进展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-21 04:00

研究人员已经证明，“梯度跨度算法”在高维度的缩放高斯随机函数上表现出可预测的行为。这一发现为大型机器学习模型在复杂地形上进行随机初始化后，多次训练运行中观察到的持续成本曲线提供了理论解释。可预测的进展现象已被自动化机器学习（AutoML）社区利用，减少了使用相同超参数进行重复训练的需要。 AI

影响为AutoML的效率提供了理论基础，可能简化模型开发。

排序理由该集群包含一篇详细介绍机器学习新理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Felix Benning, Leif D\"oring · 2026-07-21 04:00

梯度跨度算法在高维空间中取得可预测进展

arXiv:2410.09973v2 Announce Type: replace Abstract: We prove that all 'gradient span algorithms' have asymptotically deterministic behavior on scaled Gaussian random functions as the dimension tends to infinity. This is a functional generalization of similar results for random qu…