Ben Chamberlain — accepted AI-conference papers

2022

Graph-Coupled Oscillator Networks

T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, Michael Bronstein

ICML 2022spotlight

We propose Graph-Coupled Oscillator Networks (GraphCON), a novel framework for deep learning on graphs. It is based on discretizations of a second-order system of ordinary differential equations (ODEs), which model a network of nonlinear controlled and damped oscillators, coupled via the adjacency s…

Cited by 160 Source ↗PDF ↗code ↗Scholar ↗

2021

GRAND: Graph Neural Diffusion

Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb, Emanuele Rossi

ICML 2021spotlight

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE. In our model, the layer structure and topology correspond to the discretisation choices of temporal and…

Cited by 392 Source ↗PDF ↗code ↗Scholar ↗