Revisiting Bayesian Autoencoders With MCMC

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 h...

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Bibliographic Details
Published in:	IEEE access Vol. 10; pp. 40482 - 40495
Main Authors:	Chandra, Rohitash, Jain, Mahir, Maharana, Manavendra, Krivitsky, Pavel N.
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	autoencoders Bayes methods Bayesian analysis Bayesian deep learning Computational modeling Data models Decoding Deep learning Langevin dynamics Machine learning Markov chains MCMC Neural networks parallel tempering Proposals Sampling Statistical inference Uncertainty
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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