4 papers
Descending into the Modular Bootstrap
Nathan Benjamin, A. Liam Fitzpatrick, Wei Li +1
In this paper, we attempt to explore the landscape of two-dimensional conformal field theories (2d CFTs) by efficiently searching for numerical solutions to the modular bootstrap e…
Sven: Singular Value Descent as a Computationally Efficient Natural Gradient Method
Samuel Bright-Thonney, Thomas R. Harvey, Andre Lukas +1
We introduce Sven (Singular Value dEsceNt), a new optimization algorithm for neural networks that exploits the natural decomposition of loss functions into a sum over individual da…
A Lorentz-Equivariant Transformer for All of the LHC
Johann Brehmer, VÃctor Bresó, Pim de Haan +4
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Colli…
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
Jonas Spinner, Victor Bresó, Pim de Haan +3
Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorent…