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ORCIDThomas Moreau 0001
Person information
- affiliation: Université Paris-Saclay, Inria, CEA, Palaiseau, France
Other persons with the same name
- Thomas Moreau 0002 — Collecte Localisation Satellites (CLS), Toulouse, France
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2020 – today
- 2024
[c23]Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin:
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. AISTATS 2024: 82-90- [c22]Matthieu Terris, Thomas Moreau, Nelly Pustelnik, Julián Tachella:
Equivariant Plug-and-Play Image Reconstruction. CVPR 2024: 25255-25264 - [i30]Pierre Guetschel
, Thomas Moreau, Michael Tangermann:
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention. CoRR abs/2403.11772 (2024) - [i29]Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Lopes, Sebastien Velut, Salim Khazem, Thomas Moreau:
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark. CoRR abs/2404.15319 (2024) - [i28]Emilia Siviero, Guillaume Staerman, Stéphan Clémençon, Thomas Moreau:
Flexible Parametric Inference for Space-Time Hawkes Processes. CoRR abs/2406.06849 (2024) - [i27]Guillaume Staerman, Virginie Loison, Thomas Moreau:
Unmixing Noise from Hawkes Process to Model Learned Physiological Events. CoRR abs/2406.16938 (2024) - [i26]Yanis Lalou, Théo Gnassounou, Antoine Collas, Antoine de Mathelin, Oleksii Kachaiev, Ambroise Odonnat, Alexandre Gramfort, Thomas Moreau, Rémi Flamary:
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation. CoRR abs/2407.11676 (2024) - 2023
- [j8]Zaccharie Ramzi, Kevin Michalewicz, Jean-Luc Starck, Thomas Moreau, Philippe Ciuciu:
Wavelets in the Deep Learning Era. J. Math. Imaging Vis. 65(1): 240-251 (2023) - [j7]Lindsey Power, Cédric Allain, Thomas Moreau, Alexandre Gramfort, Timothy Bardouille:
Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset. NeuroImage 267: 119809 (2023) - [j6]Louis Rouillard, Alexandre Le Bris, Thomas Moreau, Demian Wassermann:
PAVI: Plate-Amortized Variational Inference. Trans. Mach. Learn. Res. 2023 (2023) - [c21]Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty:
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. ICML 2023: 2777-2805 - [c20]Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau:
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. ICML 2023: 32575-32597 - [i25]Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin:
A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. CoRR abs/2302.08766 (2023) - [i24]Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty:
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. CoRR abs/2303.05798 (2023) - [i23]Zaccharie Ramzi, Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Test like you Train in Implicit Deep Learning. CoRR abs/2305.15042 (2023) - [i22]Louis Rouillard, Alexandre Le Bris, Thomas Moreau, Demian Wassermann:
PAVI: Plate-Amortized Variational Inference. CoRR abs/2308.16022 (2023) - [i21]Matthieu Terris, Thomas Moreau:
Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers. CoRR abs/2311.18710 (2023) - [i20]Matthieu Terris, Thomas Moreau, Nelly Pustelnik, Julián Tachella:
Equivariant plug-and-play image reconstruction. CoRR abs/2312.01831 (2023) - 2022
- [j5]Thomas Moreau, Alexandre Gramfort:
DiCoDiLe: Distributed Convolutional Dictionary Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(5): 2426-2437 (2022) - [c19]Cédric Allain, Alexandre Gramfort, Thomas Moreau:
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals. ICLR 2022 - [c18]Benoît Malézieux, Thomas Moreau, Matthieu Kowalski:
Understanding approximate and unrolled dictionary learning for pattern recovery. ICLR 2022 - [c17]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. ICLR 2022 - [c16]Cédric Rommel, Thomas Moreau, Joseph Paillard, Alexandre Gramfort:
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals. ICLR 2022 - [c15]Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré la Tour, Ghislain Durif, Cássio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter:
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. NeurIPS 2022 - [c14]Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau:
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. NeurIPS 2022 - [c13]Cédric Rommel, Thomas Moreau, Alexandre Gramfort:
Deep invariant networks with differentiable augmentation layers. NeurIPS 2022 - [i19]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) - [i18]Cédric Rommel, Thomas Moreau, Alexandre Gramfort:
Deep invariant networks with differentiable augmentation layers. CoRR abs/2202.02142 (2022) - [i17]Louis Rouillard, Thomas Moreau, Demian Wassermann:
PAVI: Plate-Amortized Variational Inference. CoRR abs/2206.05111 (2022) - [i16]Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré la Tour, Ghislain Durif, Cássio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter:
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. CoRR abs/2206.13424 (2022) - [i15]Cédric Rommel, Joseph Paillard, Thomas Moreau, Alexandre Gramfort:
Data augmentation for learning predictive models on EEG: a systematic comparison. CoRR abs/2206.14483 (2022) - [i14]Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau:
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. CoRR abs/2210.04635 (2022) - 2021
- [j4]Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Claire Leroy, Philippe Ciuciu:
Multivariate semi-blind deconvolution of fMRI time series. NeuroImage 241: 118418 (2021) - [c12]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
- [j3]Gabriel A. Brat, Griffin M. Weber, Nils Gehlenborg, Paul Avillach, Nathan P. Palmer, Luca Chiovato, James J. Cimino, Lemuel R. Waitman, Gilbert S. Omenn, Alberto Malovini, Jason H. Moore, Brett K. Beaulieu-Jones, Valentina Tibollo, Shawn N. Murphy, Sehi L'Yi, Mark S. Keller, Riccardo Bellazzi, David A. Hanauer, Arnaud Serret-Larmande, Alba Gutiérrez-Sacristán, John J. Holmes, Douglas S. Bell, Kenneth D. Mandl, Robert W. Follett, Jeffrey G. Klann, Douglas A. Murad, Luigia Scudeller, Mauro Bucalo, Katie G. Kirchoff, Jean B. Craig, Jihad S. Obeid, Vianney Jouhet, Romain Griffier, Sébastien Cossin, Bertrand Moal, Lav P. Patel, Antonio Bellasi, Hans-Ulrich Prokosch, Detlef Kraska, Piotr Sliz, Amelia L. M. Tan, Kee Yuan Ngiam, Alberto Zambelli, Danielle L. Mowery, Emily Schriver, Batsal Devkota, Robert L. Bradford, Mohamad Daniar, Christel Daniel, Vincent Benoit, Romain Bey, Nicolas Paris, Patricia Serre, Nina Orlova, Julien Dubiel, Martin Hilka, Anne-Sophie Jannot, Stéphane Bréant, Judith Leblanc, Nicolas Griffon, Anita Burgun, Mélodie Bernaux, Arnaud Sandrin, Elisa Salamanca, Sylvie Cormont, Thomas Ganslandt, Tobias Gradinger, Julien Champ, Martin Boeker, Patricia Martel, Loic Esteve, Alexandre Gramfort, Olivier Grisel, Damien Leprovost, Thomas Moreau, Gaël Varoquaux, Jill-Jênn Vie, Demian Wassermann, Arthur Mensch, Charlotte Caucheteux, Christian Haverkamp, Guillaume Lemaitre, Silvano Bosari, Ian D. Krantz, Andrew M. South, Tianxi Cai, Isaac S. Kohane:
International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. npj Digit. Medicine 3 (2020) - [c11]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 - [c10]Zaccharie Ramzi, Jean-Luc Starck, Thomas Moreau, Philippe Ciuciu:
Wavelets in the Deep Learning Era. EUSIPCO 2020: 1417-1421 - [c9]Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Super-efficiency of automatic differentiation for functions defined as a minimum. ICML 2020: 32-41 - [c8]Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau:
Learning to solve TV regularised problems with unrolled algorithms. NeurIPS 2020 - [c7]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
- [j2]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) - [c6]Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Philippe Ciuciu:
fMRI BOLD signal decomposition using a multivariate low-rank model. EUSIPCO 2019: 1-5 - [c5]Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Philippe Ciuciu:
Sparsity-based Blind Deconvolution of Neural Activation Signal in FMRI. ICASSP 2019: 1323-1327 - [c4]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
- [j1]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) - [c3]Thomas Moreau, Laurent Oudre, Nicolas Vayatis:
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding. ICML 2018: 3623-3631 - [c2]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 - [c1]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)
Coauthor Index
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