| Machine learning for neuroimaging with scikit-learn A Abraham, F Pedregosa, M Eickenberg, P Gervais, A Mueller, J Kossaifi, ... Frontiers in neuroinformatics 8, 71792, 2014 | 2179 | 2014 |
| 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 | 705 | 2017 |
| 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 | 502 | 2017 |
| 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 | 338 | 2019 |
| Offline a/b testing for recommender systems A Gilotte, C Calauzènes, T Nedelec, A Abraham, S Dollé Proceedings of the Eleventh ACM International Conference on Web Search and …, 2018 | 248 | 2018 |
| Extracting brain regions from rest fMRI with total-variation constrained dictionary learning A Abraham, E Dohmatob, B Thirion, D Samaras, G Varoquaux Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013 | 77 | 2013 |
| Integrating multimodal priors in predictive models for the functional characterization of Alzheimer’s disease M Rahim, B Thirion, A Abraham, M Eickenberg, E Dohmatob, C Comtat, ... Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015 …, 2015 | 16 | 2015 |
| Loading and plotting of cortical surface representations in Nilearn J Huntenburg, A Abraham, J Loula, F Liem, K Dadi, G Varoquaux Research Ideas and Outcomes 3, e12342, 2017 | 14 | 2017 |
| Comparing functional connectivity based predictive models across datasets K Dadi, A Abraham, M Rahim, B Thirion, G Varoquaux 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 1-4, 2016 | 13 | 2016 |
| Region segmentation for sparse decompositions: better brain parcellations from rest fMRI A Abraham, E Dohmatob, B Thirion, D Samaras, G Varoquaux arXiv preprint arXiv:1412.3925, 2014 | 13 | 2014 |
| Rebuilding trust in active learning with actionable metrics A Abraham, L Dreyfus-Schmidt 2020 International Conference on Data Mining Workshops (ICDMW), 836-843, 2020 | 6 | 2020 |
| Sample noise impact on active learning A Abraham, L Dreyfus-Schmidt arXiv preprint arXiv:2109.01372, 2021 | 5 | 2021 |
| Learning functional brain atlases modeling inter-subject variability A Abraham Université Paris-Saclay, 2015 | 4 | 2015 |
| An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication M MacMahon, W Hwang, S Yim, E MacMahon, A Abraham, J Barton, ... Informatics in Medicine Unlocked 43, 101387, 2023 | 2* | 2023 |
| Cost impact of the use of a universal cardiac implantable electronic devices remote monitoring solution: results of the Evidence RM study E Marijon, E Vicaut, A Abraham, I Ibnouhsein, C Henry, G Faedda, ... European Heart Journal 45 (Supplement_1), ehae666. 3543, 2024 | 1 | 2024 |
| Towards clear expectations for uncertainty estimation V Bouvier, S Maggio, A Abraham, L Dreyfus-Schmidt arXiv preprint arXiv:2207.13341, 2022 | 1 | 2022 |
| Cardinal, a metric-based Active learning framework A Abraham, L Dreyfus-Schmidt Software Impacts 12, 100250, 2022 | 1 | 2022 |
| Impact of a universal monitoring system (“third party”) on outcomes of ICD patients: A nationwide study N Varma, E Marijon, É Vicaut, S Boveda, A Abraham, I Ibnouhsein, ... Heart Rhythm, 2024 | | 2024 |
| Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes A Abraham, AH Idrobo arXiv preprint arXiv:2407.14861, 2024 | | 2024 |
| Clinical Impact of a Universal Remote Monitoring Platform for ICD and CRT-D Follow-up from a Large Real-World Registry N Varma, E Marijon, A Abraham, I Ibnouhsein, JL Bonnet, A Rosier, ... Heart Rhythm 20 (7), 1095, 2023 | | 2023 |
Gaël VaroquauxResearch director, INRIAAdresse e-mail validée de normalesup.org