【变分贝叶斯自动编码(AEVB)】Auto-Encoding V

Auto-Encoding Variational Bayes

Diederik P Kingma,Max Welling

【论文+代码(Python):变分贝叶斯自动编码(AEVB)】《Auto-Encoding Variational Bayes》Diederik P Kingma, Max Welling (2014)O网页链接Github(fauxtograph):O网页链接参阅:O爱可可-爱生活

(Submitted on 20 Dec 2013 (v1), last revised 1 May 2014 (this version, v10))

How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.

Subjects:Machine Learning (stat.ML); Learning (cs.LG)

Cite as:arXiv:1312.6114[stat.ML]

(orarXiv:1312.6114v10[stat.ML]for this version)

Download:

PDF