36 citations · 151 across the 8 of their papers we have counts for
8 papers
Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
Sergei Manzhos, Manabu Ihara
Representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates (corresponding of different orders of coupling) are useful i…
Neural network with optimal neuron activation functions based on additive Gaussian process regression
Sergei Manzhos, Manabu Ihara
Feed-forward neural networks (NN) are a staple machine learning method widely used in many areas of science and technology. While even a single-hidden layer NN is a universal appro…
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
Kernel based methods including Gaussian process regression (GPR) and generally kernel ridge regression (KRR) have been finding increasing use in computational chemistry, including…
Non-invasive improvement of machining by reversible electrochemical doping: a proof of principle with computational modeling
Anastassia Sorkin, Yunfa Guo, Manabu Ihara +2
We propose that the machinability of hard ceramics can be improved by reversible electrochemical doping. On the example of TiO2, we show in a combined density functional theory-mol…
On the optimization of hyperparameters in Gaussian process regression with the help of low-order high-dimensional model representation
Sergei Manzhos, Manabu Ihara
When the data are sparse, optimization of hyperparameters of the kernel in Gaussian process regression by the commonly used maximum likelihood estimation (MLE) criterion often lead…
Rectangularization of Gaussian process regression for optimization of hyperparameters
Sergei Manzhos, Manabu Ihara
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 u…