Results 31 to 40 of about 105,151 (260)
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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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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Rectangularization of Gaussian process regression for optimization of hyperparameters
Machine Learning with Applications, 2023 Gaussian process regression (GPR) is a powerful machine learning method which has recently enjoyed wider use, in particular in physical sciences. In its original formulation, GPR uses a square matrix of covariances among training data and can be viewed ...
Sergei Manzhos, Manabu Ihara
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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 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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mgpr: An R package for multivariate Gaussian process regression
SoftwareX, 2023 Gaussian process regression (GPR) is a non-parametric kernel-based machine learning method. GPR is based on Bayesian formalism, which enables the estimation of prediction uncertainty of the response variables.
Petri Varvia +2 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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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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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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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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