# Revisiting Bayesian Autoencoders with MCMC

arXiv: https://arxiv.org/abs/2104.05915 · HTML: https://mlsanity.com/paper/2104.05915/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 107 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 30 of 38)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Importance Weighted Autoencoders](https://mlsanity.com/paper/1509.00519/) (2015)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Grammar Variational Autoencoder](https://mlsanity.com/paper/1703.01925/) (2017)
- [Generating 3D faces using Convolutional Mesh Autoencoders](https://mlsanity.com/paper/1807.10267/) (2018)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks](https://mlsanity.com/paper/1701.04722/) (2017)
- [Generative Probabilistic Novelty Detection with Adversarial Autoencoders](https://mlsanity.com/paper/1807.02588/) (2018)
- [GMNN: Graph Markov Neural Networks](https://mlsanity.com/paper/1905.06214/) (2019)
- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting](https://mlsanity.com/paper/2010.06772/) (2020)
- [Generalizing Hamiltonian Monte Carlo with Neural Networks](https://mlsanity.com/paper/1711.09268/) (2017)
- [Robust Anomaly Detection in Images using Adversarial Autoencoders](https://mlsanity.com/paper/1901.06355/) (2019)
- [Marginal likelihood computation for model selection and hypothesis testing: an extensive review](https://mlsanity.com/paper/2005.08334/) (2020)
- [Variational Recurrent Neural Machine Translation](https://mlsanity.com/paper/1801.05119/) (2018)
- [Denoising Adversarial Autoencoders](https://mlsanity.com/paper/1703.01220/) (2017)
- [Deep Learning: A Bayesian Perspective](https://mlsanity.com/paper/1706.00473/) (2017)
- [Revisiting the Gelman-Rubin Diagnostic](https://mlsanity.com/paper/1812.09384/) (2018)
- [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild](https://mlsanity.com/paper/1612.08534/) (2016)
- [Langevin-gradient parallel tempering for Bayesian neural learning](https://mlsanity.com/paper/1811.04343/) (2018)
- [Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1912.12175/) (2019)
- [Maximum a Posteriori Estimators as a Limit of Bayes Estimators](https://mlsanity.com/paper/1611.05917/) (2016)
- [Architectural Implications of Graph Neural Networks](https://mlsanity.com/paper/2009.00804/) (2020)
- [Autoencoder with recurrent neural networks for video forgery detection](https://mlsanity.com/paper/1708.08754/) (2017)

## Built on by (most cited first, 10 of 10)

- [A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation](https://mlsanity.com/paper/2304.02858/) (2023)
- [Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants](https://mlsanity.com/paper/2211.10575/) (2022)
- [SMOTified-GAN for class imbalanced pattern classification problems](https://mlsanity.com/paper/2108.03235/) (2021)
- [Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams](https://mlsanity.com/paper/2304.10740/) (2023)
- [Bayesian neural networks via MCMC: a Python-based tutorial](https://mlsanity.com/paper/2304.02595/) (2023)
- [Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection](https://mlsanity.com/paper/2202.12653/) (2022)
- [Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation](https://mlsanity.com/paper/2410.01847/) (2024)
- [Uniform Transformation: Refining Latent Representation in Variational Autoencoders](https://mlsanity.com/paper/2407.02681/) (2024)
- [Latent Space Perspicacity and Interpretation Enhancement (LS-PIE) Framework](https://mlsanity.com/paper/2307.05620/) (2023)
- [Convolutional neural networks for mineral prospecting through alteration mapping with remote sensing data](https://mlsanity.com/paper/2502.18533/) (2025)

## Abstract (arXiv)

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.