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.
We developed a novel uncertainty-aware framework MatPhase to predict material phases of electrodes from low contrast SEM images.
We released two open-source datasets named GDB-9-Ex and ORNL_AISD-Ex that provide calculations of electronic excitation energies and their associated oscillator strengths based on the time-dependent density-functional tight-binding (TD-DFTB) method.
A team of researchers from the 91°µÍø (ORNL) developed a novel architecture for a hybrid quantum-classical neural network.
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 from Virginia Polytechnic Institute and State University (Virginia Tech) and 91°µÍø (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.