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papersSEP 11 04:00 UTC

Sublinear Variational Optimization Scales Gaussian Mixture Models to Billions of Parameters

A revised arXiv preprint introduces a variational optimization method for training Gaussian mixture models that keeps computational cost below the usual scaling limits. The approach aims to make large, general GMMs trainable even when datasets contain many high-dimensional data points. This addresses a long-standing bottleneck that has made such models impractical at scale.

arXiv Gaussian Mixture Models Scalable Machine Learning Sublinear Algorithms Variational Optimization

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arXiv cs.LGSublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters ↗SEP 11 04:00 UTC