Random Sampling High Dimensional Model Representation Gaussian Process
Regression (RS-HDMR-GPR) for representing multidimensional functions with
machine-learned lower-dimensional terms allowing insight with a general
method

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Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method

24 November 2020

Mohamed Ali Boussaidi

Dmitry Voytsekhovsky

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Papers citing "Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method"

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Title
Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension Sergei Manzhos Manabu Ihara 99 8 0 17 Nov 2023
Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization Sergei Manzhos Manabu Ihara 41 9 0 11 Feb 2023
Neural network with optimal neuron activation functions based on additive Gaussian process regression Sergei Manzhos Manabu Ihara 62 20 0 13 Jan 2023
The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces Sergei Manzhos Manabu Ihara GP 23 6 0 21 Nov 2022