Then, we propose a meta-learning framework, to jointly train the perturbation function over heterogeneous tasks in parallel. As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize toheterogeneous tasksandarchitectures.

We propose a non-iterative method that enforces the following ideas during training.

Insuch situations, weallowthealgorithm toreturn asmall listofcandidates such that one of the candidates is close to the true parameter.

Community detection is an essential tool for unsupervised data exploration and revealing theorganisational structure ofnetworkedsystems. Withalong history innetwork science, community detection typically relies on objectivefunctions, optimised with custom-tailored search algorithms, but often without leveraging recentadvancesindeeplearning.