Web: http://arxiv.org/abs/2209.07036

Sept. 16, 2022, 1:13 a.m. | Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai, Yutaka Matsuo

stat.ML updates on arXiv.org arxiv.org

Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for
approximating intractable distributions. However, its usage is limited in the
context of deep latent variable models owing to costly datapoint-wise sampling
iterations and slow convergence. This paper proposes the amortized Langevin
dynamics (ALD), wherein datapoint-wise MCMC iterations are entirely replaced
with updates of an encoder that maps observations into latent variables. This
amortization enables efficient posterior sampling without datapoint-wise
iterations. Despite its efficiency, we prove that ALD is …


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