8 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…
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…
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…
Compressive Recovery of Sparse Precision Matrices
Titouan Vayer, Etienne Lasalle, Rémi Gribonval +1
We consider the problem of learning a graph modeling the statistical relations of the variables from a dataset with samples . Standard approa…
Eve, Adam and the Preferential Attachment Tree
Alice Contat, Nicolas Curien, Perrine Lacroix +2
We consider the problem of finding the initial vertex (Adam) in a Barabási--Albert tree process at large times. More precisely, given …
Heat diffusion distance processes: a statistically founded method to analyze graph data sets
Etienne Lasalle
We propose two multiscale comparisons of graphs using heat diffusion, allowing to compare graphs without node correspondence or even with different sizes. These multiscale comparis…