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Meta-Learning or "Learning To Learn"

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If you have a question about this talk, please contact Robert Pinsler.

This tutorial talk will introduce the concepts behind meta-learning or “Learning to Learn” with a focus on few-shot learning scenarios. Key meta-learning approaches including Model Agnostic Meta-Learning (MAML) and Prototypical Networks will be covered in detail. We’ll finish up by describing meta-learning applications such as few-shot image classification, view reconstruction, and meta-reinforcement learning.

This talk is part of the Machine Learning Reading Group @ CUED series.

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