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.


M.A. Armenta Armenta, A. Schmitt: The Hilbert-Mumford criterion for representations of network quivers. In Proceedings of the International Conference on Applied Sciences and Engineering (ICASE 2023). Atlantis Highlights in Engineering.


Related Pictures

ICASE 2023

Alexander Schmitt at the conference ICASE, Ulaanbataar, 2023