# Scalable Gaussian Process Variational Autoencoders

arXiv: https://arxiv.org/abs/2010.13472 · HTML: https://mlsanity.com/paper/2010.13472/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

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

- [Priors in Bayesian Deep Learning: A Review](https://mlsanity.com/paper/2105.06868/) (2021)
- [On the Challenges and Opportunities in Generative AI](https://mlsanity.com/paper/2403.00025/) (2024)
- [Markovian Gaussian Process Variational Autoencoders](https://mlsanity.com/paper/2207.05543/) (2022)
- [Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes](https://mlsanity.com/paper/2302.04534/) (2023)
- [On Disentanglement in Gaussian Process Variational Autoencoders](https://mlsanity.com/paper/2102.05507/) (2021)
- [Phi-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation](https://mlsanity.com/paper/2209.15609/) (2022)
- [Identifiable Feature Learning for Spatial Data with Nonlinear ICA](https://mlsanity.com/paper/2311.16849/) (2023)
- [Latent mixed-effect models for high-dimensional longitudinal data](https://mlsanity.com/paper/2409.11008/) (2024)
- [Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling](https://mlsanity.com/paper/2505.16481/) (2025)
- [Recurrent Memory for Online Interdomain Gaussian Processes](https://mlsanity.com/paper/2502.08736/) (2025)
- [Sparse Gaussian Neural Processes](https://mlsanity.com/paper/2504.01650/) (2025)
- [AR-Flow VAE: A Structured Autoregressive Flow Prior Variational Autoencoder for Unsupervised Blind Source Separation](https://mlsanity.com/paper/2603.14441/) (2026)
- [Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data](https://mlsanity.com/paper/2412.16899/) (2024)
- [When Bayesian Tensor Completion Meets Multioutput Gaussian Processes: Functional Universality and Rank Learning](https://mlsanity.com/paper/2512.21486/) (2025)
- [Amortised Inference in Bayesian Neural Networks](https://mlsanity.com/paper/2309.03018/) (2023)
- [PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders](https://mlsanity.com/paper/2505.19320/) (2025)
- [Latent-Autoregressive GP-VAE Language Model](https://mlsanity.com/paper/2512.09535/) (2025)
- [Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport](https://mlsanity.com/paper/2605.20989/) (2026)
- [Gaussian Process Prior Variational Autoencoder for Endoscopic Videos](https://mlsanity.com/paper/2606.19908/) (2026)
- [Population-Level Generative Modeling for Ranking Data](https://mlsanity.com/paper/2608.08422/) (2026)

## Abstract (arXiv)

Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GP-VAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP inference. We improve the scalability of these methods through principled sparse inference approaches. We propose a new scalable GP-VAE model that outperforms existing approaches in terms of runtime and memory footprint, is easy to implement, and allows for joint end-to-end optimization of all components.