Data-efficient and explainable machine learning

This research will investigate novel ML approaches (particularly NN based) that deliver improved performance, robustness, and interpretability particularly for problems where data is limited and noisy (contaminated). The focus will be on new neural architectures, data scheduling and pre-processing, novel training objectives and matching optimisation methods. Architectures that