70 citations · 110 across the 33 of their papers we have counts for
6 papers · 1 filter
Specific Algorithmic Interpretability of Neural Networks: A Case Study on Textures
Yanglin Zhang, Anneke von Seeger, Gilad Lerman +1
We develop a principled framework for constructing neural networks whose specific parameter realizations admit an explicit algorithmic interpretation. Existing algorithm-inspired a…
Out-of-Distribution Inverse Design of Elastic Networks with Differentiable Graph Neural Network Molecular Dynamics
Sergey A. Shteingolts, Salman N. Salman, Ron Levie +1
Machine-learning-based inverse design can accelerate the discovery of materials with targeted properties, but conventional structure--property models often require large training d…
Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
Eden Nagar, Ya-Wei Eileen Lin, Ron Levie
Graph Neural Networks (GNNs) perform computations on graphs by routing the signal between graph regions using a graph shift operator or a message passing scheme. Often, the propaga…
Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs
Antonis Vasileiou, Juan Cervino, Pascal Frossard +7
Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine lea…
A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
Ofek Amran, Tom Gilat, Ron Levie
Generalization and approximation capabilities of message passing graph neural networks (MPNNs) are often studied by defining a compact metric on a space of input graphs under which…
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…