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今天的主题是：Auto-Encoding Variational Bayes

Summary

The paper introduces a novel method for performing efficient approximate inference and learning in directed probabilistic models with continuous latent variables. This method, called Auto-Encoding Variational Bayes (AEVB), is based on a reparameterization of the variational lower bound, leading to a stochastic estimator that can be optimized using standard stochastic gradient methods. The paper demonstrates that AEVB can be used to efficiently learn the parameters of a generative model, as well as to perform inference on the latent variables. The authors also show that AEVB has theoretical advantages over other methods for performing approximate inference, and they provide experimental results that support their claims.

原文链接：arxiv.org