Results 51 to 60 of about 100,134 (161)
Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
Applied Sciences, 2022 Monitoring the rover slip is important; however, a certain level of estimation uncertainty is inevitable. In this paper, we establish slip estimation models for China’s Mars rover, Zhurong, using Gaussian process regression (GPR).
Tianyi Zhang +5 more
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Sparse Additive Gaussian Process Regression
J. Mach. Learn. Res., 2019 In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting. Motivated by the ideas of sparsification, localization and Bayesian additive modeling, our model is built around a recursive partitioning (RP) scheme. Within each RP partition, a sparse GP (SGP) regression model is fitted.
Hengrui Luo +2 more
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Communication-efficient ADMM using quantization-aware Gaussian process regression
EURO Journal on Computational OptimizationIn networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems.
Aldo Duarte +2 more
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Cross Trajectory Gaussian Process Regression Model for Battery Health Prediction
Journal of Modern Power Systems and Clean Energy, 2021 Accurate battery capacity prediction is important to ensure reliable battery operation and reduce the cost. However, the complex nature of battery degradation and the presence of capacity regeneration phenomenon render the prediction task very ...
Jianshe Feng +5 more
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Hierarchical Gaussian process mixtures for regression [PDF]
Statistics and Computing, 2005 As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields. However, two major problems arise when the model is applied to a large data-set with repeated measurements.
Jian Qing Shi +2 more
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Localization Reliability Improvement Using Deep Gaussian Process Regression Model
Sensors, 2018 With the widespread use of the Global Positioning System, indoor positioning technology has attracted increasing attention. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor ...
Fei Teng, Wenyuan Tao, Chung-Ming Own
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Gaussian Processes for Regression [PDF]
, 1995 The Bayesian analysis of neural networks is difficult because a sim ple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian anal ysis for fixed values of hyperparameters to be carried out exactly using matrix ...
Williams, C., Rasmussen, C.
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Quantum-assisted Gaussian process regression
Physical Review A, 2019 Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires $O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. Rev. Lett.
Zhikuan Zhao +2 more
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Gaussian Process Regression with Soft Equality Constraints
MathematicsThis study introduces a novel Gaussian process (GP) regression framework that probabilistically enforces physical constraints, with a particular focus on equality conditions.
Didem Kochan, Xiu Yang
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Robust and Conjugate Gaussian Process Regression
CoRR, 2023 To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and simplistic assumption is often violated in practice, which leads to unreliable inferences and uncertainty quantification.
Matías Altamirano +2 more
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