Auto-Encoding Variational Bayes

Yan-Kun Chen; Jingxuan Liu; Lingyun Peng; Yiqi Wu; Yige Xu; Zhanhao Zhang

doi:10.61603/ceas.v2i1.33

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.

Keywords

Bayes' theoremEncoding (memory)Computer scienceBayesian probabilityMathematicsComputational biologyArtificial intelligenceAlgorithmBiology

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Publication Info

Year: 2024
Type: article
Volume: 2
Issue: 1
Citations: 977
Access: Closed

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APA Style

                            
                                
                                    Yan-Kun Chen, 
                                
                                    Jingxuan Liu, 
                                
                                    Lingyun Peng
                                
                                et al.
                            
                            (2024). 
                            Auto-Encoding Variational Bayes. 
                            Cambridge Explorations in Arts and Sciences
                            , 2
                            (1)
                            .
                            https://doi.org/10.61603/ceas.v2i1.33
                        

Identifiers

DOI: 10.61603/ceas.v2i1.33