Results 41 to 50 of about 110,828 (307)
Efficient Gaussian Neural Processes for Regression
CoRR, 2021 6 ...
Markou, Stratis +3 more
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Online Spatio-Temporal Gaussian Process Experts with Application to Tactile Classification [PDF]
, 2012 16/01/14 meb. conference paper, pre-print version OK to add.In this work, we are primarily concerned with robotic systems that learn online and continuously from multi-variate data-streams.
Soh, Harold +5 more
core +1 more source
Learning Conductance: Gaussian Process Regression for Molecular Electronics
, 2022 Experimental studies of charge transport through single molecules often rely on break junction setups, where molecular junctions are repeatedly formed and broken while measuring the conductance, leading to a statistical distribution of conductance values.
Michael, Deffner +6 more
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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
doaj +1 more source
Model selection and signal extraction using Gaussian Process regression
Journal of High Energy Physics, 2023 We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the ...
Abhijith Gandrakota +3 more
doaj +1 more source
Gaussian process regression for geometry optimization [PDF]
The Journal of Chemical Physics, 2018 We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures.
Alexander Denzel, Johannes Kästner
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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
openaire +2 more sources
A Gaussian process regression (GPR) quest to predict HOMO-LUMO energy
, 2023 Machine learning methods employ statistical algorithms and pattern recognition techniques to learn patterns and make predictions based on statistical patterns.
MANJEET, BHATIA
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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
doaj +1 more source
Gaussian Process Regression Ensemble Model for Network Traffic Prediction
IEEE Access, 2020 Network traffic prediction is substantial for network optimization and resource management. However, designing an efficient predictive model considering different traffic characteristics, including periodicity, nonlinearity, and nonstationarity, is ...
Abdolkhalegh Bayati +2 more
doaj +1 more source