activity
20182020
most citedJoint learning of variational representations and solvers for inverse problems with partially-observed data

21 citations · 22 across the 4 of their papers we have counts for

collaborators

6 papers

eess.SP2020

Gradients of Connectivity as Graph Fourier Bases of Brain Activity

Giulia Lioi, Vincent Gripon, Abdelbasset Brahim +2

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neu…

cs.CV2020

A fast and memory-efficient algorithm for smooth interpolation of polyrigid transformations: application to human joint tracking

K. Makki, B. Borotikar, M. Garetier +3

The log Euclidean polyrigid registration framework provides a way to smoothly estimate and interpolate poly-rigid/affine transformations for which the invertibility is guaranteed.…

cs.LG202021 cited

Joint learning of variational representations and solvers for inverse problems with partially-observed data

Ronan Fablet, Lucas Drumetz, Francois Rousseau

Designing appropriate variational regularization schemes is a crucial part of solving inverse problems, making them better-posed and guaranteeing that the solution of the associate…

physics.med-ph20191 cited

On early brain folding patterns using biomechanical growth modeling

Xiaoyu Wang, Amine Bohi, Mariam Al Harrach +3

Abnormal cortical folding patterns may be related to neurodevelopmental disorders such as lissencephaly and polymicrogyria. In this context, computational modeling is a powerful to…

physics.bio-ph2019

Global Perturbation of Initial Geometry in a Biomechanical Model of Cortical Morphogenesis

Amine Bohi, Xiaoyu Wang, Mariam Al Harrach +3

Cortical folding pattern is a main characteristic of the geometry of the human brain which is formed by gyri (ridges) and sulci (grooves). Several biological hypotheses have sugges…

cs.CV2018

Residual Networks as Geodesic Flows of Diffeomorphisms

Francois Rousseau, Ronan Fablet

This paper addresses the understanding and characterization of residual networks (ResNet), which are among the state-of-the-art deep learning architectures for a variety of supervi…