A multidisciplinary team of researchers has developed an adaptive physics refinement (APR) technique to effectively model cancer cell transport.
Transformer language models provide state-of-the-art accuracy in a range of learning tasks, ranging from natural language processing to non-traditional applications such as molecular design.
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.