Deep Homogeneous Mixture Models: Representation, Separation, and Approximation

NeurIPS 2018 Priyank JainiPascal PoupartYaoliang Yu

At their core, many unsupervised learning models provide a compact representation of homogeneous density mixtures, but their similarities and differences are not always clearly understood. In this work, we formally establish the relationships among latent tree graphical models (including special cases such as hidden Markov models and tensorial mixture models), hierarchical tensor formats and sum-product networks... (read more)

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