| 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 | 70994 | 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 | 10207 | 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 | 2502 | 2013 |
| Machine learning for neuroimaging with scikit-learn A Abraham, F Pedregosa, M Eickenberg, P Gervais, A Mueller, J Kossaifi, ... Frontiers in neuroinformatics, 14, 2014 | 1333 | 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 | 983 | 2016 |
| Mayavi: 3D visualization of scientific data P Ramachandran, G Varoquaux Computing in Science & Engineering 13 (2), 40-51, 2011 | 696 | 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 | 526 | 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 | 506 | 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 | 486 | 2017 |
| Cross-validation failure: Small sample sizes lead to large error bars G Varoquaux Neuroimage 180, 68-77, 2018 | 479 | 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 | 467 | 2015 |
| Scikit-learn: machine learning in python J Mach Learn Res 12: 2825–2830 F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... | 412* | 2011 |
| 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 | 356 | 2017 |
| Which fMRI clustering gives good brain parcellations? B Thirion, G Varoquaux, E Dohmatob, JB Poline Frontiers in neuroscience 8, 167, 2014 | 344 | 2014 |
| Establishment of best practices for evidence for prediction: a review RA Poldrack, G Huckins, G Varoquaux JAMA psychiatry 77 (5), 534-540, 2020 | 341 | 2020 |
| 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 | 312 | 2010 |
| 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 | 289 | 2017 |
| Connectivity‐based parcellation: Critique and implications SB Eickhoff, B Thirion, G Varoquaux, D Bzdok Human brain mapping 36 (12), 4771-4792, 2015 | 269 | 2015 |
| Benchmarking functional connectome-based predictive models for resting-state fMRI K Dadi, M Rahim, A Abraham, D Chyzhyk, M Milham, B Thirion, ... NeuroImage 192, 115-134, 2019 | 230 | 2019 |
| Learning and comparing functional connectomes across subjects G Varoquaux, RC Craddock NeuroImage 80, 405-415, 2013 | 230 | 2013 |
Bertrand ThirionInriaVerified email at inria.fr
