Abstract: Methods and systems are described for selecting a group of data points to be labelled in an active learning process. The method comprises the step of determining a temperature parameter that selects between a representation-based point selection method and an uncertainty-based point selection method based on the current budget. The method also comprises the step of determining a kernel radius parameter that is inversely proportional to the size of the current budget. The method then includes the step of estimating an uncertainty coverage based on the temperature parameter, the kernel radius parameter and the current budget, wherein the uncertainty coverage is a measure of how much uncertainty is covered by the group of data points. Finally, the next point is greedily selected based on the estimated uncertainty coverage.

Abstract: Broadly speaking, the present disclosure describes a method of modeling a temporal point process (TPP). For each sequence in the TPP, the method treats the sequence as one of a plurality of distinct tasks, and applies meta learning to the distinct tasks. Applying meta learning to the distinct tasks may comprise applying a neural process to the distinct tasks; the neural process may be an attentive neural process.