| 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 | 55908 | 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 | 9006 | 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 | 1867 | 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 | 1038 | 2014 |
| 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 | 745 | 2016 |
| Mayavi: 3D visualization of scientific data P Ramachandran, G Varoquaux Computing in Science & Engineering 13 (2), 40-51, 2011 | 628 | 2011 |
| 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 | 469 | 2015 |
| 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 | 432 | 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 | 393 | 2017 |
| Cross-validation failure: Small sample sizes lead to large error bars G Varoquaux Neuroimage 180, 68-77, 2018 | 377 | 2018 |
| Scikit-learn: Machine learning without learning the machinery G Varoquaux, L Buitinck, G Louppe, O Grisel, F Pedregosa, A Mueller GetMobile: Mobile Computing and Communications 19 (1), 29-33, 2015 | 360 | 2015 |
| Which fMRI clustering gives good brain parcellations? B Thirion, G Varoquaux, E Dohmatob, JB Poline Frontiers in neuroscience 8, 167, 2014 | 314 | 2014 |
| 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, 2010 | 300 | 2010 |
| 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 | 287 | 2017 |
| Connectivity‐based parcellation: Critique and implications SB Eickhoff, B Thirion, G Varoquaux, D Bzdok Human brain mapping 36 (12), 4771-4792, 2015 | 249 | 2015 |
| Seeing it all: Convolutional network layers map the function of the human visual system M Eickenberg, A Gramfort, G Varoquaux, B Thirion NeuroImage 152, 184-194, 2017 | 239 | 2017 |
| Learning and comparing functional connectomes across subjects G Varoquaux, RC Craddock NeuroImage 80, 405-415, 2013 | 219 | 2013 |
| 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 | 207 | 2011 |
| Establishment of best practices for evidence for prediction: a review RA Poldrack, G Huckins, G Varoquaux JAMA psychiatry 77 (5), 534-540, 2020 | 203 | 2020 |
| Group-PCA for very large fMRI datasets SM Smith, A Hyvärinen, G Varoquaux, KL Miller, CF Beckmann Neuroimage 101, 738-749, 2014 | 203 | 2014 |
Bertrand ThirionInriaE-mail confirmado em inria.fr
