Gaël Varoquaux
Gaël Varoquaux
Research director, INRIA
Verified email at normalesup.org - Homepage
TitleCited byYear
Scikit-learn: Machine learning in Python
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
Journal of machine learning research 12 (Oct), 2825-2830, 2011
221332011
The NumPy array: a structure for efficient numerical computation
S Van Der Walt, SC Colbert, G Varoquaux
Computing in Science & Engineering 13 (2), 22-30, 2011
44672011
API design for machine learning software: experiences from the scikit-learn project
L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ...
arXiv preprint arXiv:1309.0238, 2013
6292013
Mayavi: 3D visualization of scientific data
P Ramachandran, G Varoquaux
Computing in Science & Engineering 13 (2), 40-51, 2011
4402011
Machine learning for neuroimaging with scikit-learn
A Abraham, F Pedregosa, M Eickenberg, P Gervais, A Mueller, J Kossaifi, ...
Frontiers in neuroinformatics 8, 14, 2014
3462014
NeuroVault. org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain
KJ Gorgolewski, G Varoquaux, G Rivera, Y Schwarz, SS Ghosh, ...
Frontiers in neuroinformatics 9, 8, 2015
2472015
Which fMRI clustering gives good brain parcellations?
B Thirion, G Varoquaux, E Dohmatob, JB Poline
Frontiers in neuroscience 8, 167, 2014
2322014
Brain covariance selection: better individual functional connectivity models using population prior
G Varoquaux, A Gramfort, JB Poline, B Thirion
Advances in neural information processing systems, 2334-2342, 2010
2242010
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments
KJ Gorgolewski, T Auer, VD Calhoun, RC Craddock, S Das, EP Duff, ...
Scientific Data 3, 160044, 2016
2162016
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
G Varoquaux, PR Raamana, DA Engemann, A Hoyos-Idrobo, Y Schwartz, ...
NeuroImage 145, 166-179, 2017
1852017
Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example
A Abraham, MP Milham, A Di Martino, RC Craddock, D Samaras, ...
NeuroImage 147, 736-745, 2017
1842017
Learning and comparing functional connectomes across subjects
G Varoquaux, RC Craddock
NeuroImage 80, 405-415, 2013
1672013
Connectivity‐based parcellation: Critique and implications
SB Eickhoff, B Thirion, G Varoquaux, D Bzdok
Human brain mapping 36 (12), 4771-4792, 2015
1612015
Multi-subject dictionary learning to segment an atlas of brain spontaneous activity
G Varoquaux, A Gramfort, F Pedregosa, V Michel, B Thirion
Biennial International Conference on Information Processing in Medical …, 2011
1402011
Total variation regularization for fMRI-based prediction of behavior
V Michel, A Gramfort, G Varoquaux, E Eger, B Thirion
IEEE transactions on medical imaging 30 (7), 1328-1340, 2011
1402011
A group model for stable multi-subject ICA on fMRI datasets
G Varoquaux, S Sadaghiani, P Pinel, A Kleinschmidt, JB Poline, B Thirion
Neuroimage 51 (1), 288-299, 2010
1362010
Group-PCA for very large fMRI datasets
SM Smith, A Hyvärinen, G Varoquaux, KL Miller, CF Beckmann
Neuroimage 101, 738-749, 2014
1222014
Scale-free and multifractal properties of fmri signals during rest and task
P Ciuciu, G Varoquaux, P Abry, S Sadaghiani, A Kleinschmidt
Frontiers in physiology 3, 186, 2012
1162012
Cross-validation failure: small sample sizes lead to large error bars
G Varoquaux
Neuroimage 180, 68-77, 2018
1042018
Predicting brain-age from multimodal imaging data captures cognitive impairment
F Liem, G Varoquaux, J Kynast, F Beyer, SK Masouleh, JM Huntenburg, ...
Neuroimage 148, 179-188, 2017
1022017
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Articles 1–20