  Learning to learn (L2L) trains a meta-learner to assist the learning of a
task-specific base learner. Previously, it was shown that a meta-learner could
learn the direct rules to update learner parameters; and that the learnt neural
optimiser updated learners more rapidly than handcrafted gradient-descent
methods. However, we demonstrate that previous neural optimisers were limited
to update learners on one designated dataset. In order to address input-domain
heterogeneity, we introduce Multi-Task Learning to Learn (MTL2L), a context
aware neural optimiser which self-modifies its optimisation rules based on
input data. We show that MTL2L is capable of updating learners to classify on
data of an unseen input-domain at the meta-testing phase.

Machine Learning;Machine Learning