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Training Energy-Based Models with Non-MCMC Samplers and Efficient Temperature Estimation
Efficient sampling from Boltzmann distributions over discrete variables is a fundamental operation in a wide range of applications. While fast non-MCMC samplers have recently emerged as promising alternatives to conventional MCMC methods, their practical use for probabilistic learning remains hinder
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