Keynote: Nirmalie Wiratunga

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"Learning to Compare with Few Data for Personalised Human Activity Recognition"


Abstract: Recent advances in meta-learning provides an interesting opportunity for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist view of learning-to-learn, such that models are rapidly transferable to related but different new tasks. Unlike task-specific model training; a meta-learner’s training instance, referred to as a meta-instance is a composite of two sets: a support set and a query set of instances. In our work, we introduce learning- to-learn personalised models from few data. We motivate our contribution through an application where personalisation plays an important role, mainly that of human activity recognition for self-management of chronic diseases.


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