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Gaussian Processes and Bayesian Inference
TopicLeading institutions, researchers & key papers
This cluster of papers focuses on the application of Gaussian Processes in machine learning, covering topics such as variational inference, sparse regression, Bayesian inference, deep learning, and probabilistic models. It also explores the use of Gaussian Processes for nonparametric methods, time series modelling, and handling big data.
12
Works
IDs:OpenAlex
How has Gaussian Processes and Bayesian Inference's publication output changed over time?
ScholarIQpublication output · 2022–2023
Output grew100% over the shown period — from 1 works in 2022 to 2 in 2023.
1
2
20222023
What are the most-cited papers on Gaussian Processes and Bayesian Inference?
ScholarIQmost cited works
Solution of physics-based Bayesian inverse problems with deep generative priors
Dhruv Patel, Deep Ray, Assad A. Oberai
S40006715. 202258 CitationsOPEN ACCESS
On the adaptation of recurrent neural networks for system identification
Marco Forgione, Aneri Muni, Dario Piga, Marco Gallieri
S51360982. 202332 CitationsOPEN ACCESS
A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows
Agnimitra Dasgupta, Dhruv Patel, Deep Ray, E. A. Johnson, Assad A. Oberai
S40006715. 202324 Citations
Where is Gaussian Processes and Bayesian Inference research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
S4000671582
S5136098232
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
Funder breakdown is a member featureSign up free to unlock
How much of the research on Gaussian Processes and Bayesian Inference is open access?
ScholarIQopen access share
67%OPEN ACCESS
Gold
0%
Green
33%
Hybrid
33%
Bronze
0%
Closed
33%
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Solution of physics-based Bayesian inverse problems with deep generative priors
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On the adaptation of recurrent neural networks for system identification
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Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
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A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows
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