8 citations · 20 across the 8 of their papers we have counts for
4 papers · 1 filter
Neural networks for neurocomputing circuits: a computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties
Ye min Thant, Methawee Nukunudompanich, Chu-Chen Chueh +2
Dedicated analog neurocomputing circuits are promising for high-throughput, low power consumption applications of machine learning (ML) and for applications where implementing a di…
Gaussian Process Regression -- Neural Network Hybrid with Optimized Redundant Coordinates
Sergei Manzhos, Manabu Ihara
Recently, a Gaussian Process Regression - neural network (GPRNN) hybrid machine learning method was proposed, which is based on additive-kernel GPR in redundant coordinates constru…
Nuclear Mass Predictions Using a Neural Network with Additive Gaussian Process Regression-Optimized Activation Functions
H. X. Liu, S. Manzhos, X. H. Wu
Nuclear masses are machine-learned as a function of proton and neutron numbers. The neural network with additive Gaussian process regression-optimized activation functions (GPR-NN)…
Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
Sergei Manzhos, Johann Luder, Pavlo Golub +1
Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (OF-DFT) holds the promise of addressing an important bottleneck in large-scale…