150 citations · 180 across the 6 of their papers we have counts for
6 papers
Tensor network representations of discrete maximum entropy distributions via mean polytopes
Alex Goessmann, Martin Eigel
We present tensor network representations for discrete maximum entropy distributions under expectation constraints. To this end, we introduce Computation-Activation Networks (CompA…
An asymptotic analysis of separating pointlike and -curvelike singularities
Van Tiep Do, Alex Goeßmann
In this paper, we present a theoretical analysis of separating images consisting of pointlike and -curvelike structures, where . Our approach is based on …
The Restricted Isometry of ReLU Networks: Generalization through Norm Concentration
Alex Goeßmann, Gitta Kutyniok
While regression tasks aim at interpolating a relation on the entire input space, they often have to be solved with a limited amount of training data. Still, if the hypothesis func…
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Marcel F. Langer, Alex Goeßmann, Matthias Rupp
Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise…
Tensor network approaches for learning non-linear dynamical laws
A. Goeßmann, M. Götte, I. Roth +3
Given observations of a physical system, identifying the underlying non-linear governing equation is a fundamental task, necessary both for gaining understanding and generating det…
Group Field Theory and Holographic Tensor Networks: Dynamical Corrections to the Ryu-Takayanagi formula
Goffredo Chirco, Alex Goeßmann, Daniele Oriti +1
We introduce group field theory networks as a generalization of spin networks and of (symmetric) random tensor networks and provide a statistical computation of the Rényi entropy f…