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This document presents a comparative analysis of two effective methods for Gaussian mixture model (GMM)-based variational inference: VIPS and iBayes-GMM. The authors demonstrate that both methods yield equivalent updates despite differing implementations and design choices, which can significantly impact the quality of learned approximations. They propose a hybrid approach that combines the strengths of both methods and release an open-source implementation that supports various design configurations, outpe

Author

S Abrar

Language

EN