The paper introduces the SGVB estimator by reparameterizing continuous approximate-posterior samples as differentiable transformations of auxiliary noise, and introduces AEVB to jointly optimize an approximate inference model and a generative model for i.i.d. data with per-datapoint continuous latent variables.

The paper introduces the SGVB estimator by reparameterizing continuous approximate-posterior samples as differentiable transformations of auxiliary noise, and introduces AEVB to jointly optimize an approximate inference model and a generative model for i.i.d. data with per-datapoint continuous latent variables. · CiteArk

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