# Variational Auto-encoded Deep Gaussian Processes

arXiv: https://arxiv.org/abs/1511.06455 · HTML: https://mlsanity.com/paper/1511.06455/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2015.

## Built on by (most cited first, 20 of 20)

- [Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning](https://mlsanity.com/paper/2106.02584/) (2021)
- [Priors in Bayesian Deep Learning: A Review](https://mlsanity.com/paper/2105.06868/) (2021)
- [Neural Methods for Amortized Inference](https://mlsanity.com/paper/2404.12484/) (2024)
- [The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective](https://mlsanity.com/paper/2106.06529/) (2021)
- [GPflux: A Library for Deep Gaussian Processes](https://mlsanity.com/paper/2104.05674/) (2021)
- [Deep Gaussian Processes: A Survey](https://mlsanity.com/paper/2106.12135/) (2021)
- [Model Selection for Bayesian Autoencoders](https://mlsanity.com/paper/2106.06245/) (2021)
- [A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes](https://mlsanity.com/paper/2309.10068/) (2023)
- [Bridge the Inference Gaps of Neural Processes via Expectation Maximization](https://mlsanity.com/paper/2501.03264/) (2025)
- [A Sparse Expansion For Deep Gaussian Processes](https://mlsanity.com/paper/2112.05888/) (2021)
- [Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference](https://mlsanity.com/paper/2202.12979/) (2022)
- [Convolutional Normalizing Flows for Deep Gaussian Processes](https://mlsanity.com/paper/2104.08472/) (2021)
- [Model Selection for Production System via Automated Online Experiments](https://mlsanity.com/paper/2105.13420/) (2021)
- [Residual Deep Gaussian Processes on Manifolds](https://mlsanity.com/paper/2411.00161/) (2024)
- [Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions](https://mlsanity.com/paper/2510.21974/) (2025)
- [Sparse Techniques for Regression in Deep Gaussian Processes](https://mlsanity.com/paper/2505.11355/) (2025)
- [Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression](https://mlsanity.com/paper/2107.08265/) (2021)
- [Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems](https://mlsanity.com/paper/2306.08445/) (2023)
- [R^2-HGP: A Double-Regularized Gaussian Process for Heterogeneous Transfer Learning](https://mlsanity.com/paper/2512.10258/) (2025)
- [Deep Gaussian Processes on Directed Acyclic Graphs](https://mlsanity.com/paper/2607.09645/) (2026)

## Abstract (arXiv)

We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron. The key aspect of this reformulation is that it prevents the proliferation of variational parameters which otherwise grow linearly in proportion to the sample size. We derive a new formulation of the variational lower bound that allows us to distribute most of the computation in a way that enables to handle datasets of the size of mainstream deep learning tasks. We show the efficacy of the method on a variety of challenges including deep unsupervised learning and deep Bayesian optimization.