5 citations · 11 across the 16 of their papers we have counts for
18 papers
Decoding Naturalistic Speech from fMRI with Temporal Modeling and Multimodal Alignment
Corentin Caris, Louis Jalouzot, Nona Rajabi +9
Reconstructing what a person perceives from their brain activity is a stringent test of whether neural recordings carry meaningful information. Naturalistic speech offers a rich te…
Cephalonauts One: A deep fMRI dataset for decoding naturalistic speech in the human brain
Antoine Collas, Louis Jalouzot, Géraud Ilinca +11
Cephalonauts One is a whole-brain 3 Tesla (3T) functional magnetic resonance imaging (fMRI) dataset recorded while subjects listened to audio podcasts. Three healthy subjects under…
Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction
Ce Ju, Antoine Collas, Florent Bouchard +1
Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) matrices are widely used for individual-level prediction, but strong performance within o…
SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
Bruno Aristimunha, Ce Ju, Antoine Collas +5
Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages. Existing impl…
Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction
Joseph Paillard, Antoine Collas, Denis A. Engemann +1
Recent advances in machine learning have greatly expanded the repertoire of predictive methods for medical imaging. However, the interpretability of complex models remains a challe…
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…
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…
PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging
Théo Gnassounou, Antoine Collas, Rémi Flamary +1
Distribution shift poses a significant challenge in machine learning, particularly in biomedical applications using data collected across different subjects, institutions, and reco…
Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment
Théo Gnassounou, Antoine Collas, Rémi Flamary +2
Machine learning applications on signals such as computer vision or biomedical data often face significant challenges due to the variability that exists across hardware devices or…
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
Yanis Lalou, Théo Gnassounou, Antoine Collas +6
Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift.…
Geodesic Optimization for Predictive Shift Adaptation on EEG data
Apolline Mellot, Antoine Collas, Sylvain Chevallier +2
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the d…
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Apolline Mellot, Antoine Collas, Sylvain Chevallier +2
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require i…
Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications
Antoine Collas, Rémi Flamary, Alexandre Gramfort
This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA), specifically addressing the challenge of limited label…
The Fisher-Rao geometry of CES distributions
Florent Bouchard, Arnaud Breloy, Antoine Collas +2
When dealing with a parametric statistical model, a Riemannian manifold can naturally appear by endowing the parameter space with the Fisher information metric. The geometry induce…
Entropic Wasserstein Component Analysis
Antoine Collas, Titouan Vayer, Rémi Flamary +1
Dimension reduction (DR) methods provide systematic approaches for analyzing high-dimensional data. A key requirement for DR is to incorporate global dependencies among original an…
Parametric information geometry with the package Geomstats
Alice Le Brigant, Jules Deschamps, Antoine Collas +1
We introduce the information geometry module of the Python package Geomstats. The module first implements Fisher-Rao Riemannian manifolds of widely used parametric families of prob…
Riemannian optimization for non-centered mixture of scaled Gaussian distributions
Antoine Collas, Arnaud Breloy, Chengfang Ren +2
This paper studies the statistical model of the non-centered mixture of scaled Gaussian distributions (NC-MSG). Using the Fisher-Rao information geometry associated to this distrib…
Robust Geometric Metric Learning
Antoine Collas, Arnaud Breloy, Guillaume Ginolhac +2
This paper proposes new algorithms for the metric learning problem. We start by noticing that several classical metric learning formulations from the literature can be viewed as mo…