A bumpy journey: exploring deep Gaussian mixture models

NeurIPS 2020

A bumpy journey: exploring deep Gaussian mixture models

Dec 06, 2020
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Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables follow a mixture of Gaussian distributions. Thus, the deep mixture model consists of a set of nested mixtures of linear models, which globally provide a nonlinear model able to describe the data in a very flexible way. In order to avoid overparameterized solutions, dimension reduction by factor models can be applied at each layer of the architecture thus resulting in deep mixtures of factor analysers. Speakers: Margot Selosse, C. Gormley, J. Jacques, C. Biernacki

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