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01/13/2023
Neural network with optimal neuron activation functions based on additive Gaussian process regression
Feed-forward neural networks (NN) are a staple machine learning method w...
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11/21/2022
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
Kernel based methods including Gaussian process regression (GPR) and gen...
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12/05/2021
Rectangularization of Gaussian process regression for optimization of hyperparameters
Optimization of hyperparameters of Gaussian process regression (GPR) det...
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11/30/2021
On the optimization of hyperparameters in Gaussian process regression with the help of low-order high-dimensional model representation
When the data are sparse, optimization of hyperparameters of the kernel ...
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11/22/2021
Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy sur
We show that Gaussian process regression (GPR) allows representing multi...
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11/24/2020
