EF1 – Extracting dynamical Laws from Complex Data



Quiver Representations in Big Data and Machine Learning

Project Heads

Alexander Schmitt

Project Members

Jorge Arturo Esquivel Araya, Jan Marten Sevenster

Project Duration

01.04.2022 − 31.03.2025

Located at

FU Berlin


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.


Related Pictures

ICASE 2023

Alexander Schmitt at the conference ICASE, Ulaanbataar, 2023