3 papers
cs.LG2026
Can Graph Learning Learn Circuits?
Chester Tan, Moritz Lampert, Courtney Maynard +3
Circuit localization is a mechanistic interpretability task whose goal is to identify a sparse subgraph of a transformer's computation graph sufficient to reproduce a particular be…
astro-ph.HE2026
Learning Neural Operator Surrogates for the Black Hole Accretion Code
Matthias Nägele, Cedric Bös, Chester Tan +3
General-relativistic magnetohydrodynamic (GR-MHD) simulations are essential for studying black hole accretion, relativistic jets, and magnetic reconnection, yet their computational…
cs.LG2024
The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
Christopher Blöcker, Chester Tan, Ingo Scholtes
Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science,…