Results 51 to 60 of about 110,828 (307)

Gaussian Process Regression with Soft Equality Constraints

Mathematics
This 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
doaj   +1 more source

Sparse Additive Gaussian Process Regression

J. Mach. Learn. Res., 2019
In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting. Motivated by the ideas of sparsification, localization and Bayesian additive modeling, our model is built around a recursive partitioning (RP) scheme. Within each RP partition, a sparse GP (SGP) regression model is fitted.
Hengrui Luo, Giovanni Nattino, Matthew T. Pratola   +2 more
openaire   +4 more sources

Localization Reliability Improvement Using Deep Gaussian Process Regression Model

Sensors, 2018
With the widespread use of the Global Positioning System, indoor positioning technology has attracted increasing attention. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor ...
Fei Teng, Wenyuan Tao, Chung-Ming Own
doaj   +1 more source

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.
openaire   +3 more sources

Structure–Function Decoupling of the Sensorimotor and Default Mode Networks in Black Americans With MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano, Giacomo Boffa, Maria Petracca, Marta Ponzano, Nicole Graziano, Claire Wigley, Claire Riley, Jonathan Howard, Pietro Bontempi, Sylvia Klineova, Fred Lublin, Matilde Inglese   +11 more
wiley   +1 more source

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, Jack K. Fitzsimons, Joseph F. Fitzsimons   +2 more
openaire   +3 more sources

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, François-Xavier Briol, Jeremias Knoblauch   +2 more
openaire   +3 more sources

Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani, Emmanuel Barbeau, Béatrice Lemesle, Emilie Milongo‐Rigal, Sophie Fernandez, Sandrine Charpentier, Jean‐François Albucher, Nicolas Raposo, Fabrice Bonneville, Patrice Péran, Jérémie Pariente   +10 more
wiley   +1 more source

Variational Bayesian multinomial probit regression with Gaussian process priors [PDF]

, 2006
It is well known in the statistics literature that augmenting binary and polychotomous response models with Gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior.
Rogers, S., Girolami, M.
core  

Communication-efficient ADMM using quantization-aware Gaussian process regression

EURO Journal on Computational Optimization
In 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, Truong X. Nghiem, Shuangqing Wei   +2 more
doaj   +1 more source