mlsanityData to 7 Oct 2026

Deep convolutional Gaussian processes

arXiv 1810.03052 ↗First posted 6 Oct 2018cs.LG stat.MLcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 2; 2023: 6; 2024: 3; 2025: 1; 2026: 2.

21 papers.

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  1. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
  2. 2021The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspectivecited by 21
  3. 2021GPflux: A Library for Deep Gaussian Processescited by 12
  4. 2021Scalable Variational Gaussian Processes via Harmonic Kernel Decompositioncited by 8
  5. 2023A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processescited by 8
  6. 2023Sparse Variational Student-t Processescited by 8
  7. 2023Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processescited by 6
  8. 2021Distributional Gaussian Process Layers for Outlier Detection in Image Segmentationcited by 4
  9. 2023Convolutional Deep Kernel Machinescited by 3
  10. 2025Tighter sparse variational Gaussian processescited by 3
  11. 2024Sparse Variational Student-t Processes for Heavy-tailed Modelingcited by 2
  12. 2021Geometry-Aware Hierarchical Bayesian Learning on Manifoldscited by 1
9 more
  1. 2022Shallow and Deep Nonparametric Convolutions for Gaussian Processescited by 1
  2. 2022Distributional Gaussian Processes Layers for Out-of-Distribution Detectioncited by 1
  3. 2023Gaussian process deconvolutioncited by 1
  4. 2023Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learningcited by 1
  5. 2021Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inferencecited by 0
  6. 2024Gaussian Process Model with Tensorial Inputs and Its Application to the Design of 3D Printed Antennascited by 0
  7. 2024Deep Q-Exponential Processescited by 0
  8. 2026Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classificationcited by 0
  9. 2026Scalable Gaussian process inference via neural feature mapscited by 0