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• W2921188227 abstract "Recently much attention has been paid to deep generative models, since they have been used to great success for variational inference, generation of complex data types, and more. In most all of these settings, the goal has been to find a particular member of that model family: optimized parameters index a distribution that is close (via a divergence or classification metric) to a target distribution. Much less attention, however, has been paid to the problem of learning a model itself. Here we introduce a two-network architecture and optimization procedure for learning intractable exponential family models (not a single distribution from those models). These exponential families are learned accurately, allowing operations like posterior inference to be executed directly and generically with an input choice of natural parameters, rather than performing inference via optimization for each particular distribution within that model." @default.
• W2921188227 created "2019-03-22" @default.
• W2921188227 creator A5030697996 @default.
• W2921188227 creator A5051980219 @default.
• W2921188227 date "2019-03-18" @default.
• W2921188227 modified "2023-09-27" @default.
• W2921188227 title "Approximating exponential family models (not single distributions) with a two-network architecture." @default.
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• W2921188227 hasPublicationYear "2019" @default.
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