| 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 | 35720 | 2011 |
| SciPy 1.0: fundamental algorithms for scientific computing in Python P Virtanen, R Gommers, TE Oliphant, M Haberland, T Reddy, ... Nature methods 17 (3), 261-272, 2020 | 2731 | 2020 |
| 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 | 1084 | 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 | 634 | 2014 |
| SymPy: symbolic computing in Python A Meurer, CP Smith, M Paprocki, O Čertík, SB Kirpichev, M Rocklin, ... PeerJ Computer Science 3, e103, 2017 | 458 | 2017 |
| 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 | 172 | 2011 |
| 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 | 156 | 2015 |
| Hyperparameter optimization with approximate gradient F Pedregosa Proceedings of the 33nd International Conference on Machine Learning, ICML …, 2016 | 137 | 2016 |
| SciPy 1.0 Contributors P Virtanen, R Gommers, TE Oliphant, M Haberland, T Reddy, ... Nat. Methods 17, 261-272, 2020 | 102 | 2020 |
| ASAGA: Asynchronous parallel SAGA R Leblond, F Pedregosa, S Lacoste-Julien Proceedings of the 20th International Conference on Artificial Intelligence …, 2017 | 95 | 2017 |
| Data-driven HRF estimation for encoding and decoding models F Pedregosa, M Eickenberg, P Ciuciu, B Thirion, A Gramfort NeuroImage 104, 209-220, 2015 | 49 | 2015 |
| Word meaning in the ventral visual path: a perceptual to conceptual gradient of semantic coding V Borghesani, F Pedregosa, M Buiatti, A Amadon, E Eger, M Piazza NeuroImage 143, 128-140, 2016 | 45 | 2016 |
| Feature extraction and supervised learning on fMRI: from practice to theory F Pedregosa-Izquierdo Université Pierre et Marie Curie-Paris VI, 2015 | 34 | 2015 |
| Improved asynchronous parallel optimization analysis for stochastic incremental methods R Leblond, F Pedregosa, S Lacoste-Julien Journal of Machine Learning Research 19 (81), 2018 | 32 | 2018 |
| Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization F Pedregosa, R Leblond, S Lacoste-Julien Advances in Neural Information Processing Systems 30, 2017 | 31 | 2017 |
| On the consistency of ordinal regression methods F Pedregosa, F Bach, A Gramfort Journal of Machine Learning Research 18, 1-35, 2017 | 31 | 2017 |
| Linearly Convergent Frank-Wolfe with Backtracking Line-Search F Pedregosa, A Askari, G Negiar, M Jaggi Proceedings of the 23rd International Conference on Artificial Intelligence …, 2020 | 18* | 2020 |
| Frank-Wolfe Splitting via Augmented Lagrangian Method G Gidel, F Pedregosa, S Lacoste-Julien Proceedings of the Twenty-First International Conference on Artificial …, 2018 | 18 | 2018 |
| The difficulty of training sparse neural networks U Evci, F Pedregosa, A Gomez, E Elsen arXiv preprint arXiv:1906.10732, 2019 | 16 | 2019 |
| Scikit-learn: machine learning in Python. 2011 F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... Mach. Learn. Res 12, 2019 | 16 | 2019 |
Alexandre GramfortINRIA, Univ. Paris-SaclayEmail verificata su inria.fr
