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Comparing functional connectivity based predictive models across datasets., , , , and . PRNI, page 1-4. IEEE, (2016)Machine learning for neuroimaging with scikit-learn., , , , , , , , and . Front. Neuroinform., (2014)Predicting brain-age from multimodal imaging data captures cognitive impairment., , , , , , , , , and 7 other author(s). NeuroImage, (2017)Benchmarking functional connectome-based predictive models for resting-state fMRI., , , , , , and . NeuroImage, (2019)Learning functional brain atlases modeling inter-subject variability. (Apprentissage d'atlas fonctionnel du cerveau modélisant la variabilité inter-individuelle).. University of Paris-Saclay, France, (2015)Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example., , , , , , and . NeuroImage, (2017)Region segmentation for sparse decompositions: better brain parcellations from rest fMRI., , , , and . CoRR, (2014)Extracting Brain Regions from Rest fMRI with Total-Variation Constrained Dictionary Learning., , , , and . MICCAI (2), volume 8150 of Lecture Notes in Computer Science, page 607-615. Springer, (2013)Offline A/B Testing for Recommender Systems., , , , and . WSDM, page 198-206. ACM, (2018)Integrating Multimodal Priors in Predictive Models for the Functional Characterization of Alzheimer's Disease., , , , , , and . MICCAI (1), volume 9349 of Lecture Notes in Computer Science, page 207-214. Springer, (2015)