mlsanityData to 7 Oct 2026

Graph Convolutional Gaussian Processes

arXiv 1905.05739 ↗First posted 14 May 2019cs.LG cs.CV stat.MLcited by 20

Not on the map. Why?

Abstract (arXiv)

We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains. The resulting graph convolutional Gaussian processes can be applied to problems in machine learning for which the input observations are functions with domains on general graphs. The structure of these models allows for high dimensional inputs while retaining expressibility, as is the case with convolutional neural networks. We present applications of graph convolutional Gaussian processes to images and triangular meshes, demonstrating their versatility and effectiveness, comparing favorably to existing methods, despite being relatively simple models.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 6; 2022: 3; 2023: 4; 2024: 5; 2025: 2.

20 papers.

Sort:

By area:

  1. 2023Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Spacecited by 40
  2. 2022NP-Match: When Neural Processes meet Semi-Supervised Learningcited by 25
  3. 2021Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structurecited by 15
  4. 2021Adaptive Gaussian Processes on Graphs via Spectral Graph Waveletscited by 11
  5. 2021PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representationcited by 9
  6. 2021Training on Test Data with Bayesian Adaptation for Covariate Shiftcited by 8
  7. 2023Bayesian Optimisation of Functions on Graphscited by 7
  8. 2022Graph Neural Networks in Computer Vision -- Architectures, Datasets and Common Approachescited by 4
  9. 2024Regularized Multi-output Gaussian Convolution Process with Domain Adaptationcited by 3
  10. 2025AdS-GNN -- a Conformally Equivariant Graph Neural Networkcited by 2
  11. 2021Evolving-Graph Gaussian Processescited by 1
  12. 2021Geometry-Aware Hierarchical Bayesian Learning on Manifoldscited by 1
8 more
  1. 2022Transductive Kernels for Gaussian Processes on Graphscited by 1
  2. 2023NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learningcited by 1
  3. 2023Gaussian process deconvolutioncited by 1
  4. 2024When narrower is better: the narrow width limit of Bayesian parallel branching neural networkscited by 1
  5. 2025Conformalized Gaussian processes for online uncertainty quantification over graphscited by 1
  6. 2024Flexible Infinite-Width Graph Convolutional Neural Networkscited by 0
  7. 2024Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimizationcited by 0
  8. 2024Exploring Probabilistic Models for Semi-supervised Learningcited by 0