In a paper accepted into the 2020 International Conference on Machine Learning, Lawrence Livermore National Laboratory researchers describe a new “Mix-n-Match” method for calibrating uncertainty of deep learning models. Uncertainty qualification is an important factor for building trust in predictive models for critical applications where accuracy and confidence are essential.