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Learning to Learn

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If you have a question about this talk, please contact Alessandro Davide Ialongo.

Abstract

Learning to Learn methods, or Meta-Learning, involve replacing hand-crafted aspects of conventional learning algorithms with more flexible features that can be learnt from data. We introduce and review recent work on learning to learn in the contexts of optimisation and few-shot learning.

Recommended Reading

There is no particular recommended reading, but the following papers will be discussed among others:
  • “Learning to learn without gradient descent by gradient descent”, Chen et al., ICML 2017
  • “Matching Networks for One Shot Learning”, Vinyals et al., NIPS 2016

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

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