Michael Eickenberg
Cited by
Cited by
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
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
Greedy Layerwise Learning Can Scale to ImageNet
E Belilovsky, M Eickenberg, E Oyallon
arXiv preprint arXiv:1812.11446, 2018
Kymatio: Scattering Transforms in Python
M Andreux, T Angles, G Exarchakis, R Leonarduzzi, G Rochette, L Thiry, ...
arXiv preprint arXiv:1812.11214, 2018
Decoupled Greedy Learning of CNNs
E Belilovsky, M Eickenberg, E Oyallon
arXiv preprint arXiv:1901.08164, 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
Data-driven HRF estimation for encoding and decoding models
F Pedregosa, M Eickenberg, P Ciuciu, B Thirion, A Gramfort
NeuroImage 104, 209-220, 2015
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
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
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
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
Feature-space selection with banded ridge regression
TD la Tour, M Eickenberg, JL Gallant
bioRxiv, 2022
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
The CAMELS project: public data release
F Villaescusa-Navarro, S Genel, D Anglés-Alcázar, LA Perez, ...
arXiv preprint arXiv:2201.01300, 2022
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
SimBIG: mock challenge for a forward modeling approach to galaxy clustering
CH Hahn, M Eickenberg, S Ho, J Hou, P Lemos, E Massara, C Modi, ...
Journal of Cosmology and Astroparticle Physics 2023 (04), 010, 2023
Parametric Scattering Networks
S Gauthier, B Thérien, L Alsène-Racicot, I Rish, E Belilovsky, ...
arXiv preprint arXiv:2107.09539, 2021
Characterizing responses of translation-invariant neurons to natural stimuli: Maximally informative invariant dimensions
M Eickenberg, RJ Rowekamp, M Kouh, TO Sharpee
Neural computation 24 (9), 2384-2421, 2012
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
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
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