Autoregressive Energy Machines
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97:17351744, 2019.
Abstract
Neural density estimators are flexible families of parametric models which have seen widespread use in unsupervised machine learning in recent years. Maximumlikelihood training typically dictates that these models be constrained to specify an explicit density. However, this limitation can be overcome by instead using a neural network to specify an energy function, or unnormalized density, which can subsequently be normalized to obtain a valid distribution. The challenge with this approach lies in accurately estimating the normalizing constant of the highdimensional energy function. We propose the Autoregressive Energy Machine, an energybased model which simultaneously learns an unnormalized density and computes an importancesampling estimate of the normalizing constant for each conditional in an autoregressive decomposition. The Autoregressive Energy Machine achieves stateoftheart performance on a suite of densityestimation tasks.
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