activity
20182021
most citedReplica-mean-field limits for intensity-based neural networks

1 citations · 2 across the 3 of their papers we have counts for

collaborators

8 papers

cs.IT20211 cited

A Random Geometric Model of Blockages in Vehicular Networks

Chang-Sik Choi, François Baccelli

This paper presents a novel spatially consistent approach for modeling line-of-sight (LOS) paths in vehicular networks. We use stochastic geometry to model transmitters, obstacles,…

math.PR2020

The Pair-Replica-Mean-Field Limit for Intensity-based Neural Networks

François Baccelli, Thibaud Taillefumier

Replica-mean-field models have been proposed to decipher the activity of neural networks via a multiply-and-conquer approach. In this approach, one considers limit networks made of…

cs.SI2019

ComHapDet: A Spatial Community Detection Algorithm for Haplotype Assembly

Abishek Sankararaman, Haris Vikalo, François Baccelli

Background: Haplotypes, the ordered lists of single nucleotide variations that distinguish chromosomal sequences from their homologous pairs, may reveal an individual's susceptibil…

math.DS20191 cited

Replica-mean-field limits for intensity-based neural networks

François Baccelli, Thibaud Taillefumier

Neural computations emerge from myriads of neuronal interactions occurring in intricate spiking networks. Due to the inherent complexity of neural models, relating the spiking acti…

math.PR2018

Doeblin Trees

François Baccelli, Mir-Omid Haji-Mirsadeghi, James T. Murphy

This paper is centered on the random graph generated by a Doeblin-type coupling of discrete time processes on a countable state space whereby when two paths meet, they merge. This…

math.PR2018

Unimodular Billingsley and Frostman Lemmas

François Baccelli, Mir-Omid Haji-Mirsadeghi, Ali Khezeli

The notions of unimodular Minkowski and Hausdorff dimensions are defined in [arXiv:1807.02980] for unimodular random discrete metric spaces. The present paper is focused on the con…