A team of researchers from Oak Ridge National Laboratory (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 Oak Ridge National Laboratory (ORNL) developed a novel architecture for a hybrid quantum-classical neural network.
Researchers from Oak Ridge National Laboratory (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 Oak Ridge National Laboratory (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 Oak Ridge National Laboratory (ORNL) propose a deep learning-based intrusion detection framework, CANShield, to detect advanced an
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.
We successfully utilized OCLF ORNL GPU computing resources for efficient uncertainty analysis, which addressed the computational overhead caused by our proposed probabilistic models.