Revisiting Bayesian Autoencoders With MCMC

doi:10.1109/ACCESS.2022.3163270

Revisiting Bayesian Autoencoders With MCMC

2022-01-01

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DOI：10.1109/ACCESS.2022.3163270

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Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust uncertainty quantification remains a challenge. This has been addressed with variational autoencoders so far. Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling has faced several limitations for large models; however, recent advances in parallel computing and advanced proposal schemes have opened routes less traveled. This paper presents Bayesian autoencoders powered by MCMC sampling implemented using parallel computing and Langevin-gradient proposal distribution. The results indicate that the proposed Bayesian autoencoder provides similar performance accuracy when compared to related methods in the literature. Furthermore, it provides uncertainty quantification in the reduced data representation. This motivates further applications of the Bayesian autoencoder framework for other deep learning models.

Keywords:

Bayes methods

Neural networks

Proposals

Deep learning

Decoding

Data models

Computational modeling

Bayesian deep learning

MCMC

Langevin dynamics

autoencoders

parallel tempering

deep learning

Revisiting Bayesian Autoencoders With MCMC

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