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20212026
most citedUniversal Approximation Theorems for Differentiable Geometric Deep Learning

11 citations · 11 across the 6 of their papers we have counts for

collaborators

6 papers

math.PR2026

Scaling limits of the single-curve interface and outermost loops in the planar random field Ising model

Fenglin Huang, Léonie Papon, Aoteng Xia

We prove that the interface separating and spins in the near-critical planar random field Ising model (RFIM) with Dobrushin boundary conditions has a scaling limit, whose…

math.PR2024

Interface scaling limit for the critical planar Ising model perturbed by a magnetic field

Léonie Papon

We prove that the interface separating and spins in the critical planar Ising model with Dobrushin boundary conditions perturbed by an external magnetic field has a scali…

math.PR2023

Massive SLE, massive CLE and the massive planar GFF

Léonie Papon

We construct a coupling between a massive GFF and a random curve in which the curve can be interpreted as the level line of the field and has the law of massive SLE. This coupl…

math.PR2023

Massive SLE and the scaling limit of the massive harmonic explorer

Léonie Papon

The massive harmonic explorer is a model of random discrete path on the hexagonal lattice that was proposed by Makarov and Smirnov as a massive perturbation of the harmonic explore…

math.PR2022

Thick points of the planar GFF are totally disconnected for all

Juhan Aru, Léonie Papon, Ellen Powell

We prove that the set of -thick points of a planar Gaussian free field (GFF) with Dirichlet boundary conditions is a.s. totally disconnected for all . Our proof relies…

cs.LG2021★ 11 cited

Universal Approximation Theorems for Differentiable Geometric Deep Learning

Anastasis Kratsios, Leonie Papon

This paper addresses the growing need to process non-Euclidean data, by introducing a geometric deep learning (GDL) framework for building universal feedforward-type models compati…