Energy-Inspired Models: Learning with Sampler-Induced Distributions

Dieterich Lawson
Dieterich Lawson

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019.

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Abstract:

Energy-based models (EBMs) are powerful probabilistic models [8, 44], 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...More

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