EF1 – Extracting dynamical Laws from Complex Data

Project

EF1-16

Quiver Representations in Big Data and Machine Learning

Project Heads

Alexander Schmitt

Project Members

N.N.

Project Duration

01.04.2022 − 31.03.2025

Located at

FU Berlin

Description

Quiver representations arise, e.g., as persistence modules in big data or as sets of weights of a neural network. We will combine algorithmic and algebro geometric methods in order to classify quiver representations arising from big data and to analyze the geometry of the moduli space of a neural network.

Related Publications

A. Schmitt: An elementary discussion of the representation and geometric
invariant theory of equioriented quivers of type D with an application to
quiver bundles. Linear Multilinear Algebra.
https://www.tandfonline.com/doi/full/10.1080/03081087.2021.1983512

 

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