| Which fMRI clustering gives good brain parcellations? B Thirion, G Varoquaux, E Dohmatob, JB Poline Frontiers in neuroscience 8, 167, 2014 | 369 | 2014 |
| Dark control: The default mode network as a reinforcement learning agent E Dohmatob, G Dumas, D Bzdok Human brain mapping 41 (12), 3318-3341, 2020 | 136* | 2020 |
| Generalized no free lunch theorem for adversarial robustness E Dohmatob arXiv preprint arXiv:1810.04065, 2018 | 103* | 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 | 79 | 2013 |
| Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping AL Pinho, A Amadon, T Ruest, M Fabre, E Dohmatob, I Denghien, ... Scientific data 5 (1), 1-15, 2018 | 72 | 2018 |
| Distributionally robust reinforcement learning E Smirnova, E Dohmatob, J Mary arXiv preprint arXiv:1902.08708, 2019 | 56 | 2019 |
| Benchmarking solvers for TV-ℓ1least-squares and logistic regression in brain imaging ED Dohmatob, A Gramfort, B Thirion, G Varoquaux 2014 International Workshop on Pattern Recognition in Neuroimaging, 1-4, 2014 | 42 | 2014 |
| Distributionally robust counterfactual risk minimization L Faury, U Tanielian, E Dohmatob, E Smirnova, F Vasile Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3850-3857, 2020 | 41 | 2020 |
| Learning nonsymmetric determinantal point processes M Gartrell, VE Brunel, E Dohmatob, S Krichene Advances in Neural Information Processing Systems 32, 2019 | 40 | 2019 |
| Inter-subject registration of functional images: do we need anatomical images? E Dohmatob, G Varoquaux, B Thirion Frontiers in neuroscience 12, 64, 2018 | 37 | 2018 |
| Learning disconnected manifolds: a no gan’s land U Tanielian, T Issenhuth, E Dohmatob, J Mary International Conference on Machine Learning, 9418-9427, 2020 | 34 | 2020 |
| Adversarial robustness via label-smoothing M Goibert, E Dohmatob arXiv preprint arXiv:1906.11567, 2019 | 28 | 2019 |
| Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping AL Pinho, A Amadon, B Gauthier, N Clairis, A Knops, S Genon, ... Scientific Data 7 (1), 353, 2020 | 25 | 2020 |
| Learning brain regions via large-scale online structured sparse dictionary learning E Dohmatob, A Mensch, G Varoquaux, B Thirion Advances in Neural Information Processing Systems 29, 2016 | 23 | 2016 |
| Brain topography beyond parcellations: local gradients of functional maps E Dohmatob, H Richard, AL Pinho, B Thirion NeuroImage 229, 117706, 2021 | 21 | 2021 |
| Grouping total variation and sparsity: Statistical learning with segmenting penalties M Eickenberg, E Dohmatob, B Thirion, G Varoquaux Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015 …, 2015 | 17 | 2015 |
| 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 |
| Scalable learning and MAP inference for nonsymmetric determinantal point processes M Gartrell, I Han, E Dohmatob, J Gillenwater, VE Brunel arXiv preprint arXiv:2006.09862, 2020 | 14 | 2020 |
| 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 |
| Subject‐specific segregation of functional territories based on deep phenotyping AL Pinho, A Amadon, M Fabre, E Dohmatob, I Denghien, JJ Torre, ... Human brain mapping 42 (4), 841-870, 2021 | 12 | 2021 |
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