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.
A multidisciplinary team of researchers from 91°µÍø (ORNL) pioneered the use of the LLVM-based high-productivity/high-performance Julia language unifying capabilities to write an end-to-end workflow on Frontier, the first US Depar
A team of researchers from 91°µÍø (ORNL) released the initial draft of the Interconnected Science Ecosystem (INTERSECT) architecture specification.
Researchers from 91°µÍø (ORNL), in collaboration with researchers from Duke University, have developed an unsupervised machine learning method, NashAE, for effective disentanglement of latent representations.
A team of researchers from 91°µÍø (ORNL), Intel Corporation and the University of Tennessee published an innovative tool-based solution to one of the most perplexing problems facing would-be users of today’s most powerful computer
A multidisciplinary team of researchers has developed an adaptive physics refinement (APR) technique to effectively model cancer cell transport.
A web-based GUI for INTERSECT has been created which allows a user to configure an experiment on an electron microscope, setting such parameters as maximum number of steps for the machine learning algorithm to perform.
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.
The team conducted numerical studies to demonstrate the connection between the parameters of neural networks and the stochastic stability of DMMs.
