Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction and Optimization

Learn about meta-learning adaptive deep kernel Gaussian processes for molecular property prediction and optimization in this one-hour seminar presentation delivered by Dr. José Miguel Hernández-Lobato from the University of Cambridge. Explore cutting-edge mathematical and statistical foundations that underpin future data-driven engineering approaches, with a specific focus on how these processes can be applied to molecular analysis. Discover advanced techniques for predicting and optimizing molecular properties using adaptive deep kernel methods combined with Gaussian processes, presented as part of the data-driven engineering seminar series at the Isaac Newton Institute.