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Mean Field Theory of NNs

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Mean Field Theory is an approximation technique from statistical physics that has recently been applied to understanding the phenomenological behaviour of neural networks. This talk will cover some background of mean field theory and two areas where it has been applied to understand and improve neural networks.

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

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