3 papers
cs.LG2026
Neural Networks With Dense Weights Are Not Universal Approximators
Levi Rauchwerger, Stefanie Jegelka, Ron Levie
We investigate the approximation capabilities of dense neural networks. While universal approximation theorems establish that sufficiently large architectures can approximate arbit…
cs.LG2025
Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
Levi Rauchwerger, Stefanie Jegelka, Ron Levie
We analyze the universality and generalization of graph neural networks (GNNs) on attributed graphs, i.e., with node attributes. To this end, we propose pseudometrics over the spac…
cs.LG2025
A Note on Graphon-Signal Analysis of Graph Neural Networks
Levi Rauchwerger, Ron Levie
A recent paper, ``A Graphon-Signal Analysis of Graph Neural Networks'', by Levie, analyzed message passing graph neural networks (MPNNs) by embedding the input space of MPNNs, i.e.…