Jonas Spinner — accepted AI-conference papers

2025

Lorentz Local Canonicalization: How to make any Network Lorentz-Equivariant

Jonas Spinner, Luigi Favaro, Peter Lippmann, Sebastian Pitz, Gerrit Gerhartz, Tilman Plehn +1

NeurIPS 2025poster

Lorentz-equivariant neural networks are becoming the leading architectures for high-energy physics. Current implementations rely on specialized layers, limiting architectural choices. We introduce Lorentz Local Canonicalization (LLoCa), a general framework that renders any backbone network exactly…

Cited by 0 Source ↗Scholar ↗

2024

Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics

Jonas Spinner, Victor Breso Pla, Pim De Haan, Tilman Plehn, Jesse Thaler, Johann Brehmer

NeurIPS 2024poster

Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architecture for high-energy physics. L-GATr represents hi…

Cited by 14 Source ↗PDF ↗code ↗Scholar ↗