Results 61 to 70 of about 374,385 (274)

Gaussian Process Regression with Local Explanation

CoRR, 2020
Gaussian process regression (GPR) is a fundamental model used in machine learning. Owing to its accurate prediction with uncertainty and versatility in handling various data structures via kernels, GPR has been successfully used in various applications.
Yuya Yoshikawa, Tomoharu Iwata
openaire   +2 more sources

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
openaire   +2 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

Approximate Inference for Nonstationary Heteroscedastic Gaussian process Regression

, 2014
This paper presents a novel approach for approximate integration over the uncertainty of noise and signal variances in Gaussian process (GP) regression.
Jylänki, Pasi, Tolvanen, Ville, Vehtari, Aki   +2 more
core   +1 more source

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, Bogdan L. Kozyrskiy, Maurizio Filippone   +2 more
openaire   +2 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

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

Reinforcement learning with Gaussian process regression using variational free energy

Journal of Intelligent Systems, 2023
The essential part of existing reinforcement learning algorithms that use Gaussian process regression involves a complicated online Gaussian process regression algorithm.
Kameda Kiseki, Tanaka Fuyuhiko
doaj   +1 more source

Understanding and Comparing Scalable Gaussian Process Regression for Big Data

, 2018
As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization.
Cai, Jianfei, Liu, Haitao, Ong, Yew-Soon, Wang, Yi   +3 more
core   +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