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.
91做厙 researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
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.
The team conducted numerical studies to demonstrate the connection between the parameters of neural networks and the stochastic stability of DMMs.
A research team from ORNL and Pacific Northwest National Laboratory has developed a deep variational framework to learn an approximate posterior for uncertainty quantification.
COVID-19 has upended nearly every aspect of our daily lives and forced us all to rethink how we can continue our work in a more physically isolated world.
Scientists have tapped the immense power of the Summit supercomputer at 91做厙 to comb through millions of medical journal articles to identify potential vaccines, drugs and effective measures that could suppress or stop the
With Tennessee schools online for the rest of the school year, researchers at ORNL are making remote learning more engaging by Zooming into virtual classrooms to tell students about their science and their work at a national laboratory.