# Deep convolutional Gaussian processes

arXiv: https://arxiv.org/abs/1810.03052 · HTML: https://mlsanity.com/paper/1810.03052/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2018.

## Built on by (most cited first, 21 of 21)

- [Priors in Bayesian Deep Learning: A Review](https://mlsanity.com/paper/2105.06868/) (2021)
- [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)
- [Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition](https://mlsanity.com/paper/2106.05992/) (2021)
- [A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes](https://mlsanity.com/paper/2309.10068/) (2023)
- [Sparse Variational Student-t Processes](https://mlsanity.com/paper/2312.05568/) (2023)
- [Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes](https://mlsanity.com/paper/2309.12658/) (2023)
- [Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation](https://mlsanity.com/paper/2104.13756/) (2021)
- [Convolutional Deep Kernel Machines](https://mlsanity.com/paper/2309.09814/) (2023)
- [Tighter sparse variational Gaussian processes](https://mlsanity.com/paper/2502.04750/) (2025)
- [Sparse Variational Student-t Processes for Heavy-tailed Modeling](https://mlsanity.com/paper/2408.06699/) (2024)
- [Geometry-Aware Hierarchical Bayesian Learning on Manifolds](https://mlsanity.com/paper/2111.00184/) (2021)
- [Shallow and Deep Nonparametric Convolutions for Gaussian Processes](https://mlsanity.com/paper/2206.08972/) (2022)
- [Distributional Gaussian Processes Layers for Out-of-Distribution Detection](https://mlsanity.com/paper/2206.13346/) (2022)
- [Gaussian process deconvolution](https://mlsanity.com/paper/2305.04871/) (2023)
- [Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning](https://mlsanity.com/paper/2311.14828/) (2023)
- [Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference](https://mlsanity.com/paper/2107.12797/) (2021)
- [Gaussian Process Model with Tensorial Inputs and Its Application to the Design of 3D Printed Antennas](https://mlsanity.com/paper/2407.15877/) (2024)
- [Deep Q-Exponential Processes](https://mlsanity.com/paper/2410.22119/) (2024)
- [Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classification](https://mlsanity.com/paper/2603.16621/) (2026)
- [Scalable Gaussian process inference via neural feature maps](https://mlsanity.com/paper/2605.10285/) (2026)

## Abstract (arXiv)

We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared to current Gaussian process approaches on the MNIST and CIFAR-10 datasets. In particular, we improve CIFAR-10 accuracy by over 10 percentage points.