“Auto-Encoding Variational Bayes” (2024), by Yan-Kun Chen et al. Computer Science research paper. Original abstract below, unchanged. No free PDF was found.

Auto-Encoding Variational Bayes

Yan-Kun Chen, Jingxuan Liu, Lingyun Peng et al. · February 7, 2024 · Computer Science

Abstract

This paper employs the Auto-Encoding Variational Bayes (AEVB) estimator based on Stochastic Gradient Variational Bayes (SGVB), designed to optimize recognition models for challenging posterior distributions and large-scale datasets. It has been applied to the mnist dataset and extended to form a Dynamic Bayesian Network (DBN) in the context of time series. The paper delves into Bayesian inference, variational methods, and the fusion of Variational Autoencoders (VAEs) and variational techniques. Emphasis is placed on reparameterization for achieving efficient optimization. AEVB employs VAEs as an approximation for intricate posterior distributions.

Source page DOI: 10.61603/ceas.v2i1.33