Results 41 to 50 of about 105,151 (260)
Engine Emission Prediction Based on Extrapolated Gaussian Process Regression Method
Shanghai Jiaotong Daxue xuebao, 2022 Aimed at improving the prediction accuracy of engine emissions under driving conditions which are not covered by the training set, an extrapolated Gaussian process regression (GPR) method is proposed.
WANG Ziyao, GUO Fengxiang, CHEN Li
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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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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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Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression
Energies, 2018 The accurate prognostics of lithium-ion battery state of health (SOH) and remaining useful life (RUL) have great significance for reducing the costs of maintenance.
Yu Peng +4 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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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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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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Gaussian Process Regression with Local Explanation
CoRR, 2020 Gaussian process regression (GPR) is a fundamental model used in machine learning. Owing to its accurate prediction with uncertainty and versatility in handling various data structures via kernels, GPR has been successfully used in various applications.
Yuya Yoshikawa, Tomoharu Iwata
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Locally Smoothed Gaussian Process Regression
Procedia Computer Science, 2022 We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation.
Davit Gogolashvili +2 more
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Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry
FEBS Letters, EarlyView.EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri +5 more
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