| Faster independent component analysis by preconditioning with Hessian approximations P Ablin, JF Cardoso, A Gramfort IEEE Transactions on Signal Processing 66 (15), 4040-4049, 2018 | 199 | 2018 |
| A framework for bilevel optimization that enables stochastic and global variance reduction algorithms M Dagréou, P Ablin, S Vaiter, T Moreau Advances in Neural Information Processing Systems 35, 2022 | 106 | 2022 |
| Statistical shape modeling of the left ventricle: myocardial infarct classification challenge A Suinesiaputra, P Ablin, X Alba, M Alessandrini, J Allen, W Bai, S Cimen, ... IEEE journal of biomedical and health informatics 22 (2), 503-515, 2017 | 96 | 2017 |
| Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states D Sabbagh, P Ablin, G Varoquaux, A Gramfort, DA Engemann NeuroImage 222, 116893, 2020 | 90 | 2020 |
| Sinkformers: Transformers with doubly stochastic attention ME Sander, P Ablin, M Blondel, G Peyré International Conference on Artificial Intelligence and Statistics, 3515-3530, 2022 | 86 | 2022 |
| Momentum residual neural networks ME Sander, P Ablin, M Blondel, G Peyré Proceedings of the 38th International Conference on Machine Learning 139, 2021 | 74 | 2021 |
| Manifold-regression to predict from MEG/EEG brain signals without source modeling D Sabbagh, P Ablin, G Varoquaux, A Gramfort, DA Engemann Advances in Neural Information Processing Systems 32 32, 7323-7334, 2019 | 69 | 2019 |
| Learning step sizes for unfolded sparse coding P Ablin, T Moreau, M Massias, A Gramfort Advances in Neural Information Processing Systems 32, 13100--13110, 2019 | 68 | 2019 |
| Faster ICA under orthogonal constraint P Ablin, JF Cardoso, A Gramfort ICASSP, 2018 | 63 | 2018 |
| Super-efficiency of automatic differentiation for functions defined as a minimum P Ablin, G Peyré, T Moreau International Conference on Machine Learning, 32-41, 2020 | 58 | 2020 |
| Kernel Stein Discrepancy Descent A Korba, PC Aubin-Frankowski, S Majewski, P Ablin Proceedings of the 38th International Conference on Machine Learning 139, 2021 | 54 | 2021 |
| Benchopt: Reproducible, efficient and collaborative optimization benchmarks T Moreau, M Massias, A Gramfort, P Ablin, PABB Charlier, M Dagréou, ... Advances in Neural Information Processing Systems 35, 2022 | 45 | 2022 |
| Do Residual Neural Networks discretize Neural Ordinary Differential Equations? ME Sander, P Ablin, G Peyré Advances in Neural Information Processing Systems 35, 2022 | 36 | 2022 |
| Modeling shared responses in neuroimaging studies through Multiview ICA H Richard, L Gresele, A Hyvärinen, B Thirion, A Gramfort, P Ablin Advances in Neural Information Processing Systems 33, 19149--19162, 2020 | 33 | 2020 |
| Fast and accurate optimization on the orthogonal manifold without retraction P Ablin, G Peyré International Conference on Artificial Intelligence and Statistics, 5636-5657, 2022 | 32 | 2022 |
| mvlearn: Multiview machine learning in python R Perry, G Mischler, R Guo, T Lee, A Chang, A Koul, C Franz, H Richard, ... Journal of Machine Learning Research 22 (109), 1-7, 2021 | 32 | 2021 |
| Beyond Pham's algorithm for joint diagonalization P Ablin, JF Cardoso, A Gramfort 27th European Symposium on Artificial Neural Networks, Computational …, 2019 | 21 | 2019 |
| Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps M Cuturi, M Klein, P Ablin Proceedings of the 40th International Conference on Machine Learning, 2023 | 18 | 2023 |
| Spectral independent component analysis with noise modeling for M/EEG source separation P Ablin, JF Cardoso, A Gramfort Journal of Neuroscience Methods 356, 109144, 2021 | 18 | 2021 |
| How to compute Hessian-vector products? M Dagréou, P Ablin, S Vaiter, T Moreau | 17 | 2024 |
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