| Scikit-learn: Machine learning in Python F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... the Journal of machine Learning research 12, 2825-2830, 2011 | 35532 | 2011 |
| 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 | 6831 | 2011 |
| 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 | 1054 | 2013 |
| 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 | 629 | 2014 |
| Mayavi: 3D visualization of scientific data P Ramachandran, G Varoquaux Computing in Science & Engineering 13 (2), 40-51, 2011 | 528 | 2011 |
| 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 (1), 1-9, 2016 | 400 | 2016 |
| 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 | 369 | 2015 |
| 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 23, 2334-2342, 2010 | 321 | 2010 |
| 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 | 291 | 2017 |
| 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 | 281 | 2017 |
| Which fMRI clustering gives good brain parcellations? B Thirion, G Varoquaux, E Dohmatob, JB Poline Frontiers in neuroscience 8, 167, 2014 | 265 | 2014 |
| Cross-validation failure: small sample sizes lead to large error bars G Varoquaux Neuroimage 180, 68-77, 2018 | 224 | 2018 |
| Connectivity‐based parcellation: Critique and implications SB Eickhoff, B Thirion, G Varoquaux, D Bzdok Human brain mapping 36 (12), 4771-4792, 2015 | 204 | 2015 |
| 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 | 195 | 2017 |
| Learning and comparing functional connectomes across subjects G Varoquaux, RC Craddock NeuroImage 80, 405-415, 2013 | 193 | 2013 |
| duchesnay E F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res 12, 2825-2830, 2010 | 177 | 2010 |
| 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 | 166 | 2011 |
| Group-PCA for very large fMRI datasets SM Smith, A Hyvärinen, G Varoquaux, KL Miller, CF Beckmann Neuroimage 101, 738-749, 2014 | 160 | 2014 |
| Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling G Varoquaux, F Baronnet, A Kleinschmidt, P Fillard, B Thirion International Conference on Medical Image Computing and Computer-Assisted …, 2010 | 160 | 2010 |
| 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 | 157 | 2011 |
Bertrand ThirionInriaVerified email at inria.fr
