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Energy-Based Models (EBMs) present a flexible and appealing way to representuncertainty. Despite recent advances, training EBMs on high-dimensional dataremains a challenging problem as the state-of-the-art approaches are costly,unstable, and require considerable tuning and domain expertise to applysuccessfully. In this work, we present a simple method for training EBMs atscale which uses an entropy-regularized generator to amortize the MCMC samplingtypically used in EBM training. We improve upon prior MCMC-based entropyregularization methods with a fast variational approximation. We demonstratethe effectiveness of our approach by using it to train tractable likelihoodmodels. Next, we apply our estimator to the recently proposed Joint EnergyModel (JEM), where we match the original performance with faster and stabletraining. This allows us to extend JEM models to semi-supervised classificationon tabular data from a variety of continuous domains.

No MCMC for me: Amortized sampling for fast and stable training of energy-based models