We developed a novel uncertainty-aware framework MatPhase to predict material phases of electrodes from low contrast SEM images.
A multidisciplinary team of researchers from Virginia Polytechnic Institute and State University (Virginia Tech) and Oak Ridge National Laboratory (ORNL) propose a deep learning-based intrusion detection framework, CANShield, to detect advanced
We present a rigorous mathematical analysis of the isolation random forest algorithm for outlier detection.
A multidisciplinary team of researchers from Oak Ridge National Laboratory (ORNL) propose a forensic framework to decide if recorded controller area network (CAN) traffic, a de facto automobile communication standard, contains masquerade attacks.
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.
Researchers at Oak Ridge National Laboratory developed a new parallel performance portable algorithm for solving the Euclidean minimum spanning tree problem (EMST), capable of processing tens of millions of data points a second.
A graph convolutional neural network (GCNN) was trained with millions of molecules to accurately predict molecular photo-optical properties by scaling data loading and training to over 1,500 GPUs on the Summit and Perlmutter supercomputers at the OLCF a
Oak Ridge National Laboratory researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
In this work we focus on dynamics problems described by waves, i.e. by hyperbolic partial differential equations.
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.