| Scaling and evaluating sparse autoencoders L Gao, T Dupré la Tour, H Tillman, G Goh, R Troll, A Radford, I Sutskever, ... arXiv preprint arXiv:2406.04093, 2024 | 127* | 2024 |
| Feature-space selection with banded ridge regression T Dupré la Tour, M Eickenberg, A Nunez-Elizalde, JL Gallant NeuroImage 264, 119728, 2022 | 79 | 2022 |
| Non-linear auto-regressive models for cross-frequency coupling in neural time series T Dupré la Tour, L Tallot, L Grabot, V Doyère, V Van Wassenhove, ... PLoS computational biology 13 (12), e1005893, 2017 | 66* | 2017 |
| Multivariate convolutional sparse coding for electromagnetic brain signals T Dupré la Tour*, T Moreau*, M Jas, A Gramfort Advances in Neural Information Processing Systems (NeurIPS), 3292-3302, 2018 | 64 | 2018 |
| Learning the morphology of brain signals using alpha-stable convolutional sparse coding M Jas*, T Dupré la Tour*, U Simsekli, A Gramfort Advances in Neural Information Processing Systems (NeurIPS), 1099-1108, 2017 | 62 | 2017 |
| Benchopt: Reproducible, efficient and collaborative optimization benchmarks T Moreau, M Massias, A Gramfort, P Ablin, PA Charlier, M Dagréou, ... Advances in Neural Information Processing Systems (NeurIPS), 2022 | 45 | 2022 |
| The strength of alpha-beta oscillatory coupling predicts motor timing precision L Grabot, TW Kononowicz, T Dupré la Tour, A Gramfort, V Doyère, ... Journal of Neuroscience, 2473-18, 2019 | 35 | 2019 |
| Semantic representations during language comprehension are affected by context F Deniz, C Tseng, L Wehbe, T Dupré la Tour, JL Gallant Journal of Neuroscience, 2023 | 29 | 2023 |
| Scikit-learn SL Developers Machine Learning in Python. Available online: https://scikit-learn. org …, 2021 | 29 | 2021 |
| scikit-learn/scikit-learn: Scikit-learn 0.22 O Grisel, A Mueller, A Gramfort, G Louppe, P Prettenhofer, M Blondel, ... Zenodo, 2020 | 19* | 2020 |
| The cortical representation of language timescales is shared between reading and listening C Chen, T Dupré la Tour, J Gallant, D Klein, F Deniz Communications Biology, 2024 | 12* | 2024 |
| scikit-learn/scikit-learn: Scikit-learn 1.4. 0 O Grisel, A Mueller, A Gramfort, G Louppe, TJ Fan, P Prettenhofer, ... Zenodo, 2024 | 10 | 2024 |
| A finer mapping of convolutional neural network layers to the visual cortex T Dupré la Tour, M Lu, M Eickenberg, JL Gallant Advances in Neural Information Processing Systems (NeurIPS), SVRHM Workshop, 2021 | 9* | 2021 |
| Transformer debugger D Mossing, S Bills, H Tillman, T Dupré la Tour, N Cammarata, L Gao, ... | 8 | 2024 |
| Gallant Lab Natural Short Clips 3T fMRI Data AG Huth, S Nishimoto, AT Vu, T Dupré la Tour, JL Gallant | 5 | 2022 |
| scikit-learn-contrib/skope-rules v1. 0.1 N Goix, V Birodkar, F Gardin, J Schertzer, H Jeong, M Kumar, A Gramfort, ... Archived software repository at https://doi. org/10.5281/zenodo 4316671, 2020 | 5 | 2020 |
| Pymoten: motion energy features from video using a pyramid of spatio-temporal gabor filters A Nunez-Elizalde, F Deniz, T Dupré la Tour, MVO Castello, JL Gallant | 4 | 2021 |
| Non-linear models for neurophysiological time series T Dupré la Tour Université Paris-Saclay, 2018 | 4* | 2018 |
| Parametric estimation of spectrum driven by an exogenous signal T Dupré la Tour, Y Grenier, A Gramfort 42nd IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 3 | 2017 |
| Driver estimation in non-linear autoregressive models T Dupré la Tour, Y Grenier, A Gramfort 43nd IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 2 | 2018 |
Alexandre GramfortMetaVerified email at meta.com