Auto-Encoding Variational Bayes 论文
2024Cambridge Explorations in Arts and Sciences引用 1003
Gaussian Processes and Bayesian InferenceGenerative Adversarial Networks and Image SynthesisBayesian Methods and Mixture Models
详细信息
- 发表期刊/会议
- Cambridge Explorations in Arts and Sciences
- 发表日期
- 2024-02-07
- 发表年份
- 2024
关键词
Gaussian Processes and Bayesian InferenceGenerative Adversarial Networks and Image SynthesisBayesian Methods and Mixture Models
摘要
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.