Generative machine learning models, including GANs (Generative Adversarial Networks), are a powerful tool toward searching chemical space for desired functionalities.
A team at ORNL has demonstrated that the combination of transfer learning and semi-supervised learning can significantly reduce the amount of labeled data required to obtain strong performance in biomedical named entity recognition (NER) tasks.
A team of researchers from 91°µÍø (ORNL) designed, implemented, and evaluated a high-performance computing (HPC) runtime system.
Researchers from 91°µÍø and the University of Central Florida have extended an evolutionary approach for training spiking neural networks.
A team of researchers from 91°µÍø applied advanced statistical methods from biomedical research to study an unexpected failure mode of general-purpose computing on graphics processing units (GPGPUs).
Researchers developed a novel algorithm for resilient and communication-efficient parallel matrix multiplication in HPC systems.
Researchers built a deep neural network to estimate the compressibility of scientific data.
To help expedite the use of quantum processing units, ORNL researchers developed an advanced software framework.