Researchers from the Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL) have developed a distributed implementation of graph convolutional neural networks [1].
A research team from ORNL, Pacific Northwest National Laboratory, and Arizona State University has developed a novel method to detect out-of-distribution (OOD) samples in continual learning without forgetting the learned knowledge of preceding tasks.
ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
Researchers at ORNL have created a unique simulation technology that allows software systems to participate in slower than real time simulation exercises, and to accomplish this without requiring recompilation of source code, relinking of object files,