The Representation Theory of Neural Networks

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• Marco Armenta (Université de Sherbrooke)
• Friday 03 December 2021, 16:00-17:00
• Seminar Room 2, Newton Institute.

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CAR - Cluster algebras and representation theory

In this talk, I will present how the representation theory of quivers can be used to study artificial neural networks. We will start by looking at why neural networks are pairs of a quiver representation and an activation function and how a neural network computes an output for a given input. We will then translate the computations of a neural network into a quiver representation and show how these induced quiver representations can be viewed inside a moduli space and finally how the training dynamics of a neural network can be translated to this moduli space.

This talk is part of the Isaac Newton Institute Seminar Series series.

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