Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document
Zewen Yang, Xiaobing Dai, Sandra Hirche
TL;DR
This paper discusses an asynchronous distributed Gaussian process regression method designed for online learning and dynamical systems, aiming to improve scalability and real-time performance in complex modeling tasks.
Contribution
It introduces a novel asynchronous distributed approach to Gaussian process regression tailored for online learning and dynamical systems, enhancing efficiency and scalability.
Findings
Improved computational efficiency in online Gaussian process regression
Enhanced scalability for large-scale dynamical systems
Potential for real-time applications in complex environments
Abstract
This is a complementary document for the paper titled "Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems".
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Taxonomy
TopicsGaussian Processes and Bayesian Inference
MethodsGaussian Process