most citedSublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters

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Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters

Sebastian Salwig, Till Kahlke, Florian Hirschberger +2

Gaussian Mixture Models (GMMs) range among the most frequently used models in machine learning. However, training large, general GMMs becomes computationally prohibitive for data s…