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Recently much attention has been paid to deep generative models, since theyhave been used to great success for variational inference, generation ofcomplex data types, and more. In most all of these settings, the goal has beento find a particular member of that model family: optimized parameters index adistribution that is close (via a divergence or classification metric) to atarget distribution. Much less attention, however, has been paid to the problemof learning a model itself. Here we introduce a two-network architecture andoptimization procedure for learning intractable exponential family models (nota single distribution from those models). These exponential families arelearned accurately, allowing operations like posterior inference to be executeddirectly and generically with an input choice of natural parameters, ratherthan performing inference via optimization for each particular distributionwithin that model.

Approximating exponential family models (not single distributions) with a two-network architecture