mlsanityData to 14 Oct 2026

Deep Gaussian Processes: A Survey

arXiv 2106.12135 ↗First posted 21 Jun 2021cs.LG stat.MLcited by 13

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Abstract (arXiv)

Gaussian processes are one of the dominant approaches in Bayesian learning. Although the approach has been applied to numerous problems with great success, it has a few fundamental limitations. Multiple methods in literature have addressed these limitations. However, there has not been a comprehensive survey of the topics as of yet. Most existing surveys focus on only one particular variant of Gaussian processes and their derivatives. This survey details the core motivations for using Gaussian processes, their mathematical formulations, limitations, and research themes that have flourished over the years to address said limitations. Furthermore, one particular research area is Deep Gaussian Processes (DGPs), it has improved substantially in the past decade. The significant publications that advanced the forefront of this research area are outlined in their survey. Finally, a brief discussion on open problems and research directions for future work is presented at the end.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 13 of its 39 references that are in the map. First posted on arXiv 21 Jun 2021.

Builds on

13 of 39 references.

References · 13

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2017Deep Neural Networks as Gaussian Processescited by 622
  3. 2013Gaussian Processes for Big Datacited by 383
  4. 2014Stochastic Gradient Hamiltonian Monte Carlocited by 343
  5. 2018Neural Processescited by 311
  6. 2019Attentive Neural Processescited by 283
  7. 2019Continual learning with hypernetworkscited by 273
  8. 2018When Gaussian Process Meets Big Data: A Review of Scalable GPscited by 206
  9. 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
  10. 2016Stochastic Variational Deep Kernel Learningcited by 93
  11. 2018Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlocited by 32
  12. 2015Variational Auto-encoded Deep Gaussian Processescited by 20
1 more
  1. 2020Energy-Based Processes for Exchangeable Datacited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 1; 2024: 2; 2025: 4; 2026: 1.

13 papers.

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  1. 2022Bayesian Learning for Neural Networks: an algorithmic surveycited by 23
  2. 2023Uncertainty Estimation for Molecules: Desiderata and Methodscited by 11
  3. 2021Multi-Task Neural Processescited by 7
  4. 2024Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulationcited by 4
  5. 2025Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantificationcited by 4
  6. 2024Knowledge-aware Evolutionary Graph Neural Architecture Searchcited by 3
  7. 2022Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimizationcited by 2
  8. 2022Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learningcited by 2
  9. 2025Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processescited by 2
  10. 2025SGPMIL: Sparse Gaussian Process Multiple Instance Learningcited by 2
  11. 2022Deep Gaussian Processes for Air Quality Inferencecited by 1
  12. 2025INR-Bench: A Unified Benchmark for Implicit Neural Representations in Multi-Domain Regression and Reconstructioncited by 1
1 more
  1. 2026MORE-PLR: multi-output regression employed for partial label rankingcited by 0