| 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 | 2171 | 2014 |
| 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 | 443 | 2017 |
| Greedy Layerwise Learning Can Scale to ImageNet E Belilovsky, M Eickenberg, E Oyallon arXiv preprint arXiv:1812.11446, 2018 | 226 | 2018 |
| Kymatio: Scattering Transforms in Python M Andreux, T Angles, G Exarchakis, R Leonarduzzi, G Rochette, L Thiry, ... arXiv preprint arXiv:1812.11214, 2018 | 206 | 2018 |
| Decoupled Greedy Learning of CNNs E Belilovsky, M Eickenberg, E Oyallon arXiv preprint arXiv:1901.08164, 2019 | 131 | 2019 |
| Solid harmonic wavelet scattering for predictions of molecule properties M Eickenberg, G Exarchakis, M Hirn, S Mallat, L Thiry The Journal of Chemical Physics 148 (24), 241732, 2018 | 95 | 2018 |
| Data-driven HRF estimation for encoding and decoding models F Pedregosa, M Eickenberg, P Ciuciu, B Thirion, A Gramfort NeuroImage 104, 209-220, 2015 | 83 | 2015 |
| Feature-space selection with banded ridge regression TD la Tour, M Eickenberg, JL Gallant bioRxiv, 2022 | 79 | 2022 |
| The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence F Villaescusa-Navarro, S Genel, D Angles-Alcazar, L Thiele, R Dave, ... arXiv preprint arXiv:2109.10915, 2021 | 76 | 2021 |
| Formal models of the network co-occurrence underlying mental operations D Bzdok, G Varoquaux, O Grisel, M Eickenberg, C Poupon, B Thirion PLoS computational biology 12 (6), e1004994, 2016 | 76 | 2016 |
| Solid harmonic wavelet scattering: Predicting quantum molecular energy from invariant descriptors of 3D electronic densities M Eickenberg, G Exarchakis, M Hirn, S Mallat Advances in Neural Information Processing Systems, 6540-6549, 2017 | 68 | 2017 |
| xval: A continuous number encoding for large language models S Golkar, M Pettee, M Eickenberg, A Bietti, M Cranmer, G Krawezik, ... arXiv preprint arXiv:2310.02989, 2023 | 55 | 2023 |
| Semi-supervised factored logistic regression for high-dimensional neuroimaging data D Bzdok, M Eickenberg, O Grisel, B Thirion, G Varoquaux Advances in neural information processing systems, 3348-3356, 2015 | 54 | 2015 |
| Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging O Benkarim, C Paquola, B Park, V Kebets, SJ Hong, R Vos de Wael, ... PLoS biology 20 (4), e3001627, 2022 | 52* | 2022 |
| The CAMELS project: public data release F Villaescusa-Navarro, S Genel, D Anglés-Alcázar, LA Perez, ... arXiv preprint arXiv:2201.01300, 2022 | 50 | 2022 |
| Multiple physics pretraining for physical surrogate models M McCabe, BRS Blancard, LH Parker, R Ohana, M Cranmer, A Bietti, ... arXiv preprint arXiv:2310.02994, 2023 | 47 | 2023 |
| Cosmological Information in the Marked Power Spectrum of the Galaxy Field E Massara, F Villaescusa-Navarro, CH Hahn, MM Abidi, M Eickenberg, ... arXiv preprint arXiv:2206.01709, 2022 | 39 | 2022 |
| Robust simulation-based inference in cosmology with Bayesian neural networks P Lemos, M Cranmer, M Abidi, CH Hahn, M Eickenberg, E Massara, ... Machine Learning: Science and Technology 4 (1), 01LT01, 2023 | 34 | 2023 |
| Parametric Scattering Networks S Gauthier, B Thérien, L Alsène-Racicot, I Rish, E Belilovsky, ... arXiv preprint arXiv:2107.09539, 2021 | 25 | 2021 |
| Grouping total variation and sparsity: statistical learning with segmenting penalties M Eickenberg, E Dohmatob, B Thirion, G Varoquaux International Conference on Medical Image Computing and Computer-Assisted …, 2015 | 25* | 2015 |
