English(EN) Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters

新算法大幅缩短GMM训练时间，支持十亿参数模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一种新颖的高斯混合模型（GMM）变分近似方法，显著降低了计算复杂度。该新算法在数据维度上呈线性扩展，在组件数量和数据点数量上呈次线性扩展，相比以往方法有了实质性改进。该方法已通过实验验证，在零样本图像去噪等任务中实现了数量级的加速，并能够训练拥有超过100亿参数的GMM模型，在大型数据集上取得了最先进的性能。 AI

影响能够训练更大的GMM模型，可能提高复杂数据分析和生成任务的性能。

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在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Sebastian Salwig, Till Kahlke, Florian Hirschberger, Dennis Forster, J\"org L\"ucke · 2026-09-11 04:00

具有数百万到数十亿参数的高斯混合模型的亚线性变分优化

arXiv:2501.12299v3 Announce Type: replace Abstract: Gaussian Mixture Models (GMMs) range among the most frequently used models in machine learning. However, training large, general GMMs becomes computationally prohibitive for data sets that have many data points $N$ of high-dimen…

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报道来源 [1]

具有数百万到数十亿参数的高斯混合模型的亚线性变分优化

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