Energy-Inspired Models: Learning with Sampler-Induced Distributions
pp. 8499-8511, 2019.
Energy-based models (EBMs) are powerful probabilistic models, but suffer from intractable sampling and density evaluation due to the partition function. As a result, inference in EBMs relies on approximate sampling algorithms, leading to a mismatch between the model and inference. Motivated by this, we consider the sampler-induced distr...More
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