Results 41 to 50 of about 100,134 (161)
Non-Gaussian Gaussian Processes for Few-Shot Regression
CoRR, 2021 Gaussian Processes (GPs) have been widely used in machine learning to model distributions over functions, with applications including multi-modal regression, time-series prediction, and few-shot learning. GPs are particularly useful in the last application since they rely on Normal distributions and enable closed-form computation of the posterior ...
Sendera, Marcin +7 more
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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
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Latent Gaussian Process Regression
CoRR, 2017 We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the training data. We show how our approach can
Erik Bodin +2 more
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Gaussian Process Regression for Binned Data [PDF]
CoRR, 2018 10 pages (+1 supp), 4 ...
Smith, M.T. +2 more
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Humanoid environmental perception with Gaussian process regression
International Journal of Advanced Robotic Systems, 2016 Nowadays, humanoids are increasingly expected acting in the real world to complete some high-level tasks humanly and intelligently. However, this is a hard issue due to that the real world is always extremely complicated and full of miscellaneous ...
Dingsheng Luo +6 more
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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
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Regression with Gaussian Processes [PDF]
, 1997 The Bayesian analysis of neural networks is difficult because the prior over functions has a complex form, leading to implementations that either make approximations or use Monte Carlo integration techniques. In this paper I investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis to be carried out ...
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Efficient Gaussian Neural Processes for Regression
CoRR, 2021 6 ...
Markou, Stratis +3 more
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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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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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