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 collaboration between scientists at Oak Ridge National Laboratory (ORNL) and University of Maryland/NIST developed a theoretical approach to combine different quantum noise reduction techniques to reduce the measurement-added noise in optomechanical s
Members and students of the Computational Urban Sciences group demonstrated a method for generating scenarios of urban neighborhood growth based on existing physical structures and placement of buildings in neighborhoods.
A multidisciplinary team of researchers from Oak Ridge National Laboratory (ORNL) developed a new online heatmap method, named hilomap, to visualize geospatial datasets as online map layers when low and high trends are equally important to map users.
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
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.
We propose a novel deep learning method that achieves 170X average speed up compared to the original probabilistic marching cubes algorithm [1] implementation and performs predictions with an accuracy comparable to the original algorithm.