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

MTL2L: A Context Aware Neural Optimiser