Energy-InspiredModels: Learningwith Sampler-InducedDistributions

This yields a class ofenergy-inspired models(EIMs) that incorporate learned energyfunctions while stillproviding exactsamples andtractable log-likelihood lower bounds. We describe and evaluate three instantiations of such models based ontruncated rejection sampling, self-normalized importance sampling, and Hamiltonian importance sampling.