most citedRiemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry

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cs.LG2026

Learning fMRI activations dictionaries across individual geometries via optimal transport

Sonia Mazelet, Rémi Flamary, Bertrand Thirion

Dictionary learning is a powerful tool for creating interpretable representations. When applied to functional magnetic resonance imaging (fMRI) data, the resulting patterns of brai…

cs.LG2026

GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation

Nicolas Salvy, Hugues Talbot, Bertrand Thirion

Generative model evaluation commonly relies on high-dimensional embedding spaces to compute distances between samples. We show that dataset representations in these spaces are affe…

cs.LG2025

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs

Sonia Mazelet, Rémi Flamary, Bertrand Thirion

Optimal transport between graphs, based on Gromov-Wasserstein and other extensions, is a powerful tool for comparing and aligning graph structures. However, solving the associated…

cs.LG2025

Enhanced Generative Model Evaluation with Clipped Density and Coverage

Nicolas Salvy, Hugues Talbot, Bertrand Thirion

Although generative models have made remarkable progress in recent years, their use in critical applications has been hindered by an inability to reliably evaluate the quality of t…

cs.LG2025★ 1 cited

Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry

Antoine Collas, Ce Ju, Nicolas Salvy +1

Generating realistic brain connectivity matrices is key to analyzing population heterogeneity in brain organization, understanding disease, and augmenting data in challenging class…

cs.LG2025

An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeon

Abhishek Jana, Moeumu Uili, James Atherton +3

This paper presents a largely automated one-shot bird call classification pipeline, incorporating targeted manual quality control steps, designed for rare species absent from large…