A multidisciplinary team of researchers from 91°µÍø (ORNL) and other institutions created a Machine Learning (ML) library for the training of classifiers on spectrographic chemical data.
ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
The team conducted numerical studies to demonstrate the connection between the parameters of neural networks and the stochastic stability of DMMs.
