5 papers
Topological Spatial Graph Coarsening
Anna Calissano, Etienne Lasalle
Spatial graphs are particular graphs for which the nodes are localized in space (e.g., public transport network, molecules, branching biological structures). In this work, we consi…
Joint Reproduction Number and Spatial Connectivity Structure Estimation via Graph Sparsity-Promoting Penalized Functional
Etienne Lasalle, Barbara Pascal
During an epidemic outbreak, decision makers crucially need accurate and robust tools to monitor the pathogen propagation. The effective reproduction number, defined as the expecte…
PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms
Etienne Lasalle, Rémi Vaudaine, Titouan Vayer +4
Clustering the nodes of a graph is a cornerstone of graph analysis and has been extensively studied. However, some popular methods are not suitable for very large graphs: e.g., spe…
A multilevel approach to accelerate the training of Transformers
Guillaume Lauga, Maël Chaumette, Edgar Desainte-Maréville +2
In this article, we investigate the potential of multilevel approaches to accelerate the training of transformer architectures. Using an ordinary differential equation (ODE) interp…
A note on the relations between mixture models, maximum-likelihood and entropic optimal transport
Titouan Vayer, Etienne Lasalle
This note aims to demonstrate that performing maximum-likelihood estimation for a mixture model is equivalent to minimizing over the parameters an optimal transport problem with en…