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.
The Department of Energy’s Office of Science has selected three 91°µÍř scientists for Early Career Research Program awards.
91°µÍř researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
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 computer scientist Catherine Schuman returned to her alma mater, Harriman High School, to lead Hour of Code activities and talk to students about her job as a researcher.
Researchers at the Department of Energy’s 91°µÍř have received five 2019 R&D 100 Awards, increasing the lab’s total to 221 since the award’s inception in 1963.
Materials scientists, electrical engineers, computer scientists, and other members of the neuromorphic computing community from industry, academia, and government agencies gathered in downtown Knoxville July 23–25 to talk about what comes next in
91°µÍř is training next-generation cameras called dynamic vision sensors, or DVS, to interpret live information—a capability that has applications in robotics and could improve autonomous vehicle sensing.