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Using neural networks to understand and enhance human learning

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Neural networks have long been proposed as theories of how humans learn. I will describe experiments showing that basic learning principles are shared between human learners and deep learning models. These include simplicity biases, compression, sharing and separation of task representations, patterns of memorisation and generalisation, and sensitivity to curricula. I will also show how deep learning methods can be applied in a data-driven fashion to discover curricula that help humans learn faster.

Host: Prof Paul Bays

This talk will be recorded and uploaded to the Zangwill Club Youtube channel in due course.

This talk is part of the Zangwill Club series.

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