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2020 – today
- 2022
[j8]Thomas Moreau
, Alexandre Gramfort:
DiCoDiLe: Distributed Convolutional Dictionary Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(5): 2426-2437 (2022)- [j7]Adriana M. Santos-Ferreira, José C. B. da Silva, Jorge M. Magalhães, Samira Amraoui, Thomas Moreau, Claire Maraldi, François Boy, Nicolas Picot, Franck Borde:
Effects of Surface Wave Breaking Caused by Internal Solitary Waves in SAR Altimeter: Sentinel-3 Copernicus Products and Advanced New Products. Remote. Sens. 14(3): 587 (2022) - [i16]Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau:
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. CoRR abs/2201.13409 (2022) - [i15]Cédric Rommel, Thomas Moreau, Alexandre Gramfort:
Deep invariant networks with differentiable augmentation layers. CoRR abs/2202.02142 (2022) - [i14]Louis Rouillard, Thomas Moreau, Demian Wassermann:
PAVI: Plate-Amortized Variational Inference. CoRR abs/2206.05111 (2022) - 2021
- [j6]Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Claire Leroy, Philippe Ciuciu:
Multivariate semi-blind deconvolution of fMRI time series. NeuroImage 241: 118418 (2021) - [c15]Pedro Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort:
HNPE: Leveraging Global Parameters for Neural Posterior Estimation. NeurIPS 2021: 13432-13443 - [i13]Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort:
Leveraging Global Parameters for Flow-based Neural Posterior Estimation. CoRR abs/2102.06477 (2021) - [i12]Zaccharie Ramzi, Florian Mannel, Shaojie Bai, Jean-Luc Starck, Philippe Ciuciu, Thomas Moreau:
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models. CoRR abs/2106.00553 (2021) - [i11]Benoît Malézieux, Thomas Moreau, Matthieu Kowalski:
Dictionary and prior learning with unrolled algorithms for unsupervised inverse problems. CoRR abs/2106.06338 (2021) - [i10]Cédric Rommel, Thomas Moreau, Alexandre Gramfort:
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals. CoRR abs/2106.13695 (2021) - [i9]Cédric Allain, Alexandre Gramfort, Thomas Moreau, A. Preprint:
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals. CoRR abs/2112.06652 (2021) - 2020
- [j5]Sébastien Clerc, Craig Donlon, Franck Borde, Nicolas Lamquin, Samuel E. Hunt, Dave Smith, Malcolm McMillan, Jonathan P. D. Mittaz, Emma Woolliams, Matthew L. Hammond, Christopher J. Banks, Thomas Moreau, Bruno Picard, Matthias Raynal, Pierre Rieu, Adrien Guérou:
Benefits and Lessons Learned from the Sentinel-3 Tandem Phase. Remote. Sens. 12(17): 2668 (2020) - [c14]Clément Lalanne, Maxence Rateaux, Laurent Oudre, Matthieu P. Robert, Thomas Moreau:
Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding. EMBC 2020: 928-931 - [c13]Zaccharie Ramzi, Jean-Luc Starck, Thomas Moreau, Philippe Ciuciu:
Wavelets in the Deep Learning Era. EUSIPCO 2020: 1417-1421 - [c12]Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Super-efficiency of automatic differentiation for functions defined as a minimum. ICML 2020: 32-41 - [c11]José C. B. da Silva, Adriana M. Santos-Ferreira, Pierre Rieu, Thomas Moreau, Franck Borde, Francois Boy, Claire Maraldi, Nicolas Picot, Craig Donlon:
Detection of Internal Solitary Waves with Conventional and Advanced SAR Altimetry Processing Methods: Preliminary Results. IGARSS 2020: 3521-3524 - [c10]Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau:
Learning to solve TV regularised problems with unrolled algorithms. NeurIPS 2020 - [c9]Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux:
NeuMiss networks: differentiable programming for supervised learning with missing values. NeurIPS 2020 - [i8]Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Super-efficiency of automatic differentiation for functions defined as a minimum. CoRR abs/2002.03722 (2020) - [i7]Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux:
Neumann networks: differential programming for supervised learning with missing values. CoRR abs/2007.01627 (2020) - [i6]Clément Lalanne, Maxence Rateaux, Laurent Oudre, Matthieu P. Robert, Thomas Moreau:
Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding. CoRR abs/2011.14962 (2020)
2010 – 2019
- 2019
- [j4]Charles Truong, Rémi Barrois-Müller, Thomas Moreau, Clément Provost, Aliénor Vienne-Jumeau, Albane Moreau, Pierre-Paul Vidal, Nicolas Vayatis, Stéphane Buffat, Alain P. Yelnik, Damien Ricard, Laurent Oudre:
A Data Set for the Study of Human Locomotion with Inertial Measurements Units. Image Process. Line 9: 381-390 (2019) - [c8]Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Philippe Ciuciu:
fMRI BOLD signal decomposition using a multivariate low-rank model. EUSIPCO 2019: 1-5 - [c7]Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Philippe Ciuciu:
Sparsity-based Blind Deconvolution of Neural Activation Signal in FMRI. ICASSP 2019: 1323-1327 - [c6]Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort:
Learning step sizes for unfolded sparse coding. NeurIPS 2019: 13100-13110 - [i5]Thomas Moreau, Alexandre Gramfort:
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals. CoRR abs/1901.09235 (2019) - [i4]Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort:
Learning step sizes for unfolded sparse coding. CoRR abs/1905.11071 (2019) - 2018
- [j3]Laurent Oudre, Rémi Barrois, Thomas Moreau, Charles Truong, Aliénor Vienne-Jumeau, Damien Ricard, Nicolas Vayatis, Pierre-Paul Vidal:
Template-Based Step Detection with Inertial Measurement Units. Sensors 18(11): 4033 (2018) - [c5]Thomas Moreau, Laurent Oudre, Nicolas Vayatis:
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding. ICML 2018: 3623-3631 - [c4]Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort:
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals. NeurIPS 2018: 3296-3306 - [i3]Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort:
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals. CoRR abs/1805.09654 (2018) - 2017
- [b1]Thomas Moreau:
Convolutional Sparse Representations - application to physiological signals and interpretability for Deep Learning. (Représentations Convolutives Parcimonieuses - application aux signaux physiologiques et interpétabilité de l'apprentissage profond). University of Paris-Saclay, France, 2017 - [j2]Francois Boy, Jean-Damien Desjonqueres, Nicolas Picot, Thomas Moreau, Matthias Raynal:
CryoSat-2 SAR-Mode Over Oceans: Processing Methods, Global Assessment, and Benefits. IEEE Trans. Geosci. Remote. Sens. 55(1): 148-158 (2017) - [c3]Thomas Moreau, Joan Bruna:
Understanding Trainable Sparse Coding with Matrix Factorization. ICLR (Poster) 2017 - [i2]Thomas Moreau, Laurent Oudre, Nicolas Vayatis:
Distributed Convolutional Sparse Coding. CoRR abs/1705.10087 (2017) - 2016
- [i1]Thomas Moreau, Julien Audiffren:
Post Training in Deep Learning with Last Kernel. CoRR abs/1611.04499 (2016) - 2015
- [j1]Abderrahim Halimi, Corinne Mailhes, Jean-Yves Tourneret, Francois Boy, Thomas Moreau:
Including Antenna Mispointing in a Semi-Analytical Model for Delay/Doppler Altimetry. IEEE Trans. Geosci. Remote. Sens. 53(2): 598-608 (2015) - 2014
- [c2]Abderrahim Halimi, Corinne Mailhes, Jean-Yves Tourneret, Thomas Moreau, Francois Boy:
Exploiting time and frequency information for Delay/Doppler altimetry. EUSIPCO 2014: 1088-1092 - [c1]Abderrahim Halimi, Corinne Mailhes, Jean-Yves Tourneret, Francois Boy, Thomas Moreau:
A generalized semi-analytical model for delay/Doppler altimetry. IGARSS 2014: 5126-5129
Coauthor Index
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