K\"ahler Geometry of Quiver Varieties and Machine Learning

• URL: http://arxiv.org/abs/2101.11487v2
• Date: Wed, 10 Feb 2021 16:09:49 GMT
• Title: K\"ahler Geometry of Quiver Varieties and Machine Learning
• Authors: George Jeffreys and Siu-Cheong Lau
• Abstract summary: We develop an algebro-geometric formulation for neural networks in machine learning using the moduli space of framed representations quiver. We prove the universal approximation theorem for the multi-variable activation function constructed from the complex projective space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop an algebro-geometric formulation for neural networks in machine learning using the moduli space of framed quiver representations. We find natural Hermitian metrics on the universal bundles over the moduli which are compatible with the GIT quotient construction by the general linear group, and show that their Ricci curvatures give a K\"ahler metric on the moduli. Moreover, we use toric moment maps to construct activation functions, and prove the universal approximation theorem for the multi-variable activation function constructed from the complex projective space.

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