A General Framework for Curve and Surface Comparison and Registration with Oriented Varifolds. - Irene Kaltenmark; Benjamin Charlier; Nicolas Charon (CVPR, 2017)

Parallel Transport in Shape Analysis: A Scalable Numerical Scheme. - Maxime Louis; Alexandre Bône; Benjamin Charlier; Stanley Durrleman (GSI, 2017)

Prediction of the Progression of Subcortical Brain Structures in Alzheimer's Disease from Baseline. - Alexandre Bône; Maxime Louis; Alexandre Routier; Jorge Samper-González; Michael Bacci; Benjamin Charlier; Olivier Colliot; Stanley Durrleman (GRAIL/MFCA/MICGen@MICCAI, 2017)

Optimal Transport for Diffeomorphic Registration. - Jean Feydy; Benjamin Charlier; François-Xavier Vialard; Gabriel Peyré (MICCAI (1), 2017)

White Matter Fiber Segmentation Using Functional Varifolds. - Kuldeep Kumar; Pietro Gori; Benjamin Charlier; Stanley Durrleman; Olivier Colliot; Christian Desrosiers (GRAIL/MFCA/MICGen@MICCAI, 2017)

The Fshape Framework for the Variability Analysis of Functional Shapes. - Benjamin Charlier; Nicolas Charon; Alain Trouvé (Found. Comput. Math., 2017)

Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework. - Sieun Lee; Nicolas Charon; Benjamin Charlier; Karteek Popuri; Evgeniy Lebed; Marinko V. Sarunic; Alain Trouvé; Mirza Faisal Beg (Medical Image Anal., 2017)

Geodesic Discriminant Analysis for Manifold-Valued Data. - Maxime Louis; Benjamin Charlier; Stanley Durrleman (CVPR Workshops, 2018)

Metamorphoses of Functional Shapes in Sobolev Spaces. - Nicolas Charon; Benjamin Charlier; Alain Trouvé (Found. Comput. Math., 2018)

A Fanning Scheme for the Parallel Transport along Geodesics on Riemannian Manifolds. - Maxime Louis; Benjamin Charlier; Paul Jusselin; Susovan Pal; Stanley Durrleman (SIAM J. Numer. Anal., 2018)

Riemannian Geometry Learning for Disease Progression Modelling. - Maxime Louis; Raphaël Couronné; Igor Koval; Benjamin Charlier; Stanley Durrleman (IPMI, 2019)

Fast geometric learning with symbolic matrices. - Jean Feydy; Joan Alexis Glaunès; Benjamin Charlier; Michael M. Bronstein (NeurIPS, 2020)

IMODAL: Creating Learnable User-Defined Deformation Models. - Leander Lacroix; Benjamin Charlier; Alain Trouvé; Barbara Gris (CVPR, 2021)

Transporting Deformations of Face Emotions in the Shape Spaces: A Comparison of Different Approaches. - Paolo Piras; Valerio Varano; Maxime Louis; Antonio Profico; Stanley Durrleman; Benjamin Charlier; Franco Milicchio; Luciano Teresi (J. Math. Imaging Vis., 2021)

Kernel Operations on the GPU, with Autodiff, without Memory Overflows. - Benjamin Charlier; Jean Feydy; Joan Alexis Glaunès; François-David Collin; Ghislain Durif (J. Mach. Learn. Res., 2021)

Benchopt: Reproducible, efficient and collaborative optimization benchmarks. - Thomas Moreau 001; Mathurin Massias; Alexandre Gramfort; Pierre Ablin; Pierre-Antoine Bannier; Benjamin Charlier; Mathieu Dagréou; Tom Dupré la Tour; Ghislain Durif; Cássio F. Dantas; Quentin Klopfenstein; Johan Larsson 002; et al. (NeurIPS, 2022)

Improve learning combining crowdsourced labels by weighting Areas Under the Margin. - Tanguy Lefort; Benjamin Charlier; Alexis Joly; Joseph Salmon (CoRR, 2022)

Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it? - Tanguy Lefort; Antoine Affouard; Benjamin Charlier; Jean-Christophe Lombardo; Mathias Chouet; Hervé Goëau; Joseph Salmon; Pierre Bonnet; Alexis Joly (CoRR, 2024)

Identify Ambiguous Tasks Combining Crowdsourced Labels by Weighting Areas Under the Margin. - Tanguy Lefort; Benjamin Charlier; Alexis Joly; Joseph Salmon (Trans. Mach. Learn. Res., 2024)