Results 41 to 50 of about 374,385 (274)
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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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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Energy-Driven Image Interpolation Using Gaussian Process Regression
Journal of Applied Mathematics, 2012 Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing.
Lingling Zi, Junping Du
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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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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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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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Transport Gaussian Processes for Regression
CoRR, 2020 Gaussian process (GP) priors are non-parametric generative models with appealing modelling properties for Bayesian inference: they can model non-linear relationships through noisy observations, have closed-form expressions for training and inference, and are governed by interpretable hyperparameters.
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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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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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