The proposed method is parallelizable over a large number of computation units. Simulation results confirm the benefits of the proposed scalable GP model over ...
The proposed method is parallelizable over a large number of computation units. Simulation results confirm the benefits of the proposed scalable GP model over ...
1. INTRODUCTION AND RELATED WORKS. Gaussian process (GP) model is a class of important Bayesian. non-parametric models for machine learning and tightly related ...
A Scalable GP Model for Processing Big Datasets. A novel scalable GP regression model, which is parallelizable over a large number of computation units.
Scalable Gaussian Process Using Inexact ADMM For Big Data. Date: May 15, 2019. An introduction of our work on “Scalable Gaussian Process Using Inexact ADMM ...
Scalable Gaussian Process Using Inexact Admm for Big Data · When Gaussian Process Meets Big Data: A Review of Scalable GPs.
Paper Title: Scalable Gaussian Process Using Inexact ADMM for Big Data ; Authors: Yue Xu; Beijing University of Posts and Telecommunications ; Feng Yin; The ...
Scalable Gaussian Process Using Inexact Admm for Big Data. record by Shuguang Cui • Scalable Gaussian Process Using Inexact Admm for Big Data. Yue Xu, Feng ...
Jun 26, 2021 · In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned ...
Missing: Inexact Admm
Integrating Gaussian processes (GP) as a learning component to the Alternating Direction Method of Multipliers (ADMM) has proven effective in learning each ...
Missing: Scalable Big