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.