activity
20212026
most citedTransforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory

14 citations · 32 across the 7 of their papers we have counts for

collaborators

8 papers

hep-ph2026

Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding

Justin Berman, Francois Charton, Andres Luna +2

In this paper, we use machine learning to discover a new seeding strategy for integration-by-parts reduction of Feynman integrals, which is a frequent bottleneck in state-of-the-ar…

cs.LG2025

Int2Int: a framework for mathematics with transformers

François Charton

This paper documents Int2Int, an open source code base for using transformers on problems of mathematical research, with a focus on number theory and other problems involving integ…

hep-th2025

Recurrent Features of Amplitudes in Planar Super Yang-Mills Theory

Tianji Cai, François Charton, Kyle Cranmer +3

The planar three-gluon form factor for the chiral stress tensor operator in planar maximally supersymmetric Yang-Mills theory is an analog of the Higgs-to-three-gluon scattering am…

hep-ph2024★ 2 cited

Extrapolating Jet Radiation with Autoregressive Transformers

Anja Butter, François Charton, Javier Mariño Villadamigo +3

Generative networks are an exciting tool for fast LHC event fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of partic…

hep-ph2024★ 5 cited

Is Tokenization Needed for Masked Particle Modelling?

Matthew Leigh, Samuel Klein, François Charton +5

In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets releva…

cs.LG2024★ 14 cited

Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory

Tianji Cai, Garrett W. Merz, François Charton +4

We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills theory is a close cousin to th…

cs.LG2023

Learning the greatest common divisor: explaining transformer predictions

François Charton

The predictions of small transformers, trained to calculate the greatest common divisor (GCD) of two positive integers, can be fully characterized by looking at model inputs and ou…

cs.LG2021★ 11 cited

Linear algebra with transformers

François Charton

Transformers can learn to perform numerical computations from examples only. I study nine problems of linear algebra, from basic matrix operations to eigenvalue decomposition and i…