Jean Barbier

For four decades, statistical physics has been providing a framework to analyze neural networks. A long-standing question remained on its capacity to tackle deep-learning models capturing rich feature-learning effects, thus going beyond the narrow networks or kernel methods analyzed until now. We positively answer through the study of the supervised learning of a multilayer perceptron.

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