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2020 – today
- 2023
[j40]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)- [c79]Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau:
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. ICML 2023: 32575-32597 - [c78]Ambroise Heurtebise, Pierre Ablin, Alexandre Gramfort:
Multiview Independent Component Analysis with Delays. MLSP 2023: 1-6 - [i76]Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen:
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation. CoRR abs/2301.09696 (2023) - [i75]Théo Gnassounou, Rémi Flamary, Alexandre Gramfort:
Convolutional Monge Mapping Normalization for learning on biosignals. CoRR abs/2305.18831 (2023) - [i74]Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues:
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference. CoRR abs/2306.03580 (2023) - [i73]Bruno Aristimunha, Raphael Y. de Camargo, Walter Hugo Lopez Pinaya, Sylvain Chevallier, Alexandre Gramfort, Cédric Rommel:
Evaluating the structure of cognitive tasks with transfer learning. CoRR abs/2308.02408 (2023) - [i72]Ambroise Heurtebise, Pierre Ablin, Alexandre Gramfort:
MultiView Independent Component Analysis with Delays. CoRR abs/2312.00484 (2023) - 2022
- [j39]Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning. J. Mach. Learn. Res. 23: 149:1-149:43 (2022) - [j38]Alexander Rockhill, Eric Larson, Brittany Stedelin, Alessandra Mantovani, Ahmed M. Raslan, Alexandre Gramfort, Nicole C. Swann:
Intracranial Electrode Location and Analysis in MNE-Python. J. Open Source Softw. 7(69): 3897 (2022) - [j37]Britta U. Westner, Sarang S. Dalal, Alexandre Gramfort, Vladimir Litvak, John C. Mosher, Robert Oostenveld, Jan-Mathijs Schoffelen:
A unified view on beamformers for M/EEG source reconstruction. NeuroImage 246: 118789 (2022) - [j36]Hubert J. Banville, Sean U. N. Wood, Chris Aimone, Denis-Alexander Engemann, Alexandre Gramfort:
Robust learning from corrupted EEG with dynamic spatial filtering. NeuroImage 251: 118994 (2022) - [j35]Denis A. Engemann, Apolline Mellot, Richard Höchenberger, Hubert J. Banville, David Sabbagh, Lukas Gemein, Tonio Ball, Alexandre Gramfort:
A reusable benchmark of brain-age prediction from M/EEG resting-state signals. NeuroImage 262: 119521 (2022) - [j34]Thomas Moreau, Alexandre Gramfort:
DiCoDiLe: Distributed Convolutional Dictionary Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(5): 2426-2437 (2022) - [c77]Kenan Sehic, Alexandre Gramfort, Joseph Salmon, Luigi Nardi:
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso. AutoML 2022: 2/1-24 - [c76]Cédric Allain, Alexandre Gramfort, Thomas Moreau:
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals. ICLR 2022 - [c75]Cédric Rommel, Thomas Moreau, Joseph Paillard, Alexandre Gramfort:
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals. ICLR 2022 - [c74]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 - [c73]Juliette Millet, Charlotte Caucheteux, Pierre Orhan, Yves Boubenec, Alexandre Gramfort, Ewan Dunbar, Christophe Pallier, Jean-Remi King:
Toward a realistic model of speech processing in the brain with self-supervised learning. NeurIPS 2022 - [c72]Cédric Rommel, Thomas Moreau, Alexandre Gramfort:
Deep invariant networks with differentiable augmentation layers. NeurIPS 2022 - [c71]Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen:
The optimal noise in noise-contrastive learning is not what you think. UAI 2022: 307-316 - [i71]Cédric Rommel, Thomas Moreau, Alexandre Gramfort:
Deep invariant networks with differentiable augmentation layers. CoRR abs/2202.02142 (2022) - [i70]Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup, Alexandre Gramfort, Sylvain Chevallier, Vinay Jayaram, Camille Jeunet, Stylianos Bakas, Siegfried Ludwig, Konstantinos Barmpas, Mehdi Bahri, Yannis Panagakis, Nikolaos A. Laskaris, Dimitrios A. Adamos, Stefanos Zafeiriou, William C. Duong, Stephen M. Gordon, Vernon J. Lawhern, Maciej Sliwowski, Vincent Rouanne, Piotr Tempczyk:
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets. CoRR abs/2202.12950 (2022) - [i69]Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen:
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think. CoRR abs/2203.01110 (2022) - [i68]Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments. CoRR abs/2203.05813 (2022) - [i67]Juliette Millet, Charlotte Caucheteux, Pierre Orhan, Yves Boubenec, Alexandre Gramfort, Ewan Dunbar, Christophe Pallier, Jean-Remi King:
Toward a realistic model of speech processing in the brain with self-supervised learning. CoRR abs/2206.01685 (2022) - [i66]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) - [i65]Cédric Rommel, Joseph Paillard, Thomas Moreau, Alexandre Gramfort:
Data augmentation for learning predictive models on EEG: a systematic comparison. CoRR abs/2206.14483 (2022) - [i64]Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau:
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. CoRR abs/2210.04635 (2022) - [i63]Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues:
Validation Diagnostics for SBI algorithms based on Normalizing Flows. CoRR abs/2211.09602 (2022) - 2021
- [j33]Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T. H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer:
POT: Python Optimal Transport. J. Mach. Learn. Res. 22: 78:1-78:8 (2021) - [j32]Ronan Perry, Gavin Mischler, Richard Guo, Theo Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein:
mvlearn: Multiview Machine Learning in Python. J. Mach. Learn. Res. 22: 109:1-109:7 (2021) - [c70]Alexandre Gramfort, Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis A. Engemann:
Learning with self-supervision on EEG data. BCI 2021: 1-2 - [c69]Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King:
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects. EMNLP (Findings) 2021: 3635-3644 - [c68]Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King:
Disentangling syntax and semantics in the brain with deep networks. ICML 2021: 1336-1348 - [c67]Maëliss Jallais, Pedro L. C. Rodrigues, Alexandre Gramfort, Demian Wassermann:
Cytoarchitecture Measurements in Brain Gray Matter Using Likelihood-Free Inference. IPMI 2021: 191-202 - [c66]Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup, Alexandre Gramfort, Sylvain Chevallier, Vinay Jayaram, Camille Jeunet, Stylianos Bakas, Siegfried Ludwig, Konstantinos Barmpas, Mehdi Bahri, Yannis Panagakis, Nikolaos A. Laskaris, Dimitrios A. Adamos, Stefanos Zafeiriou, William C. Duong, Stephen M. Gordon, Vernon J. Lawhern, Maciej Sliwowski, Vincent Rouanne, Piotr Tempczyk:
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets. NeurIPS (Competition and Demos) 2021: 205-219 - [c65]Pedro Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort:
HNPE: Leveraging Global Parameters for Neural Posterior Estimation. NeurIPS 2021: 13432-13443 - [c64]Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen:
Shared Independent Component Analysis for Multi-Subject Neuroimaging. NeurIPS 2021: 29962-29971 - [i62]Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort:
Leveraging Global Parameters for Flow-based Neural Posterior Estimation. CoRR abs/2102.06477 (2021) - [i61]Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion:
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference. CoRR abs/2102.10964 (2021) - [i60]Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King:
Decomposing lexical and compositional syntax and semantics with deep language models. CoRR abs/2103.01620 (2021) - [i59]Omar Chehab, Alexandre Défossez, Jean-Christophe Loiseau, Alexandre Gramfort, Jean-Remi King:
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals. CoRR abs/2103.02339 (2021) - [i58]Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. CoRR abs/2105.01637 (2021) - [i57]Hubert J. Banville, Sean U. N. Wood, Chris Aimone, Denis-Alexander Engemann, Alexandre Gramfort:
Robust learning from corrupted EEG with dynamic spatial filtering. CoRR abs/2105.12916 (2021) - [i56]Cédric Rommel, Thomas Moreau, Alexandre Gramfort:
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals. CoRR abs/2106.13695 (2021) - [i55]Charlotte Caucheteux, Alexandre Gramfort, Jean-Rémi King:
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects. CoRR abs/2110.06078 (2021) - [i54]Marc-Andre Schulz, Bertrand Thirion, Alexandre Gramfort, Gaël Varoquaux, Danilo Bzdok:
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction. CoRR abs/2110.06135 (2021) - [i53]Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen:
Shared Independent Component Analysis for Multi-Subject Neuroimaging. CoRR abs/2110.13502 (2021) - [i52]Kenan Sehic, Alexandre Gramfort, Joseph Salmon, Luigi Nardi:
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso. CoRR abs/2111.02790 (2021) - [i51]Maëliss Jallais, Pedro Rodrigues, Alexandre Gramfort, Demian Wassermann:
Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements. CoRR abs/2111.08693 (2021) - [i50]Charlotte Caucheteux, Alexandre Gramfort, Jean-Remi King:
Long-range and hierarchical language predictions in brains and algorithms. CoRR abs/2111.14232 (2021) - [i49]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
- [j31]Amit Jaiswal, Jukka Nenonen, Matti Stenroos, Alexandre Gramfort, Sarang S. Dalal, Britta U. Westner, Vladimir Litvak, John C. Mosher, Jan-Mathijs Schoffelen, Caroline Witton, Robert Oostenveld, Lauri Parkkonen:
Comparison of beamformer implementations for MEG source localization. NeuroImage 216: 116797 (2020) - [j30]Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort:
Multi-subject MEG/EEG source imaging with sparse multi-task regression. NeuroImage 220: 116847 (2020) - [j29]David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann:
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. NeuroImage 222: 116893 (2020) - [j28]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 Schiver, 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) - [c63]Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Spatio-temporal alignments: Optimal transport through space and time. AISTATS 2020: 1695-1704 - [c62]Mathurin Massias, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon:
Support recovery and sup-norm convergence rates for sparse pivotal estimation. AISTATS 2020: 2655-2665 - [c61]Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation of Lasso-type models for hyperparameter optimization. ICML 2020: 810-821 - [c60]Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Debiased Sinkhorn barycenters. ICML 2020: 4692-4701 - [c59]Jérôme-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion:
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso. NeurIPS 2020 - [c58]Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin:
Modeling Shared responses in Neuroimaging Studies through MultiView ICA. NeurIPS 2020 - [i48]Mathurin Massias, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon:
Support recovery and sup-norm convergence rates for sparse pivotal estimation. CoRR abs/2001.05401 (2020) - [i47]Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation of Lasso-type models for hyperparameter optimization. CoRR abs/2002.08943 (2020) - [i46]Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Debiased Sinkhorn barycenters. CoRR abs/2006.02575 (2020) - [i45]Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin:
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA. CoRR abs/2006.06635 (2020) - [i44]Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, Alexandre Gramfort:
Uncovering the structure of clinical EEG signals with self-supervised learning. CoRR abs/2007.16104 (2020) - [i43]Jérôme-Alexis Chevalier, Alexandre Gramfort, Joseph Salmon, Bertrand Thirion:
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso. CoRR abs/2009.14310 (2020) - [i42]Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon, Samuel Vaiter:
Model identification and local linear convergence of coordinate descent. CoRR abs/2010.11825 (2020) - [i41]Pedro L. C. Rodrigues, Alexandre Gramfort:
Learning summary features of time series for likelihood free inference. CoRR abs/2012.02807 (2020)
2010 – 2019
- 2019
- [j27]Stefan Appelhoff, Matthew Sanderson, Teon Brooks, Marijn van Vliet, Romain Quentin, Chris Holdgraf, Maximilien Chaumon, Ezequiel Mikulan, Kambiz Tavabi, Richard Höchenberger, Dominik Welke, Clemens Brunner, Alexander Rockhill, Eric Larson, Alexandre Gramfort, Mainak Jas:
MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. J. Open Source Softw. 4(44): 1896 (2019) - [c57]Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Wasserstein regularization for sparse multi-task regression. AISTATS 2019: 1407-1416 - [c56]Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis R. Bach:
Stochastic algorithms with descent guarantees for ICA. AISTATS 2019: 1564-1573 - [c55]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Beyond Pham's algorithm for joint diagonalization. ESANN 2019 - [c54]Pierre Ablin, Dylan Fagot, Herwig Wendt, Alexandre Gramfort, Cédric Févotte:
A Quasi-Newton Algorithm on the Orthogonal Manifold for NMF with Transform Learning. ICASSP 2019: 700-704 - [c53]Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort:
Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates. IPMI 2019: 743-754 - [c52]Hubert J. Banville, Graeme Moffat, Isabela Albuquerque, Denis-Alexander Engemann, Aapo Hyvärinen, Alexandre Gramfort:
Self-Supervised Representation Learning from Electroencephalography Signals. MLSP 2019: 1-6 - [c51]Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon:
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso. NeurIPS 2019: 3961-3972 - [c50]David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann:
Manifold-regression to predict from MEG/EEG brain signals without source modeling. NeurIPS 2019: 7321-7332 - [c49]Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort:
Learning step sizes for unfolded sparse coding. NeurIPS 2019: 13100-13110 - [i40]Yannick Roy, Hubert J. Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H. Falk, Jocelyn Faubert:
Deep learning-based electroencephalography analysis: a systematic review. CoRR abs/1901.05498 (2019) - [i39]Thomas Moreau, Alexandre Gramfort:
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals. CoRR abs/1901.09235 (2019) - [i38]Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon:
Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise. CoRR abs/1902.02509 (2019) - [i37]Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort:
Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates. CoRR abs/1902.04812 (2019) - [i36]Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort:
Learning step sizes for unfolded sparse coding. CoRR abs/1905.11071 (2019) - [i35]David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann:
Manifold-regression to predict from MEG/EEG brain signals without source modeling. CoRR abs/1906.02687 (2019) - [i34]Mathurin Massias, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Dual Extrapolation for Sparse Generalized Linear Models. CoRR abs/1907.05830 (2019) - [i33]Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort:
Multi-subject MEG/EEG source imaging with sparse multi-task regression. CoRR abs/1910.01914 (2019) - [i32]Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Spatio-Temporal Alignments: Optimal transport through space and time. CoRR abs/1910.03860 (2019) - [i31]Hubert J. Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort:
Self-supervised representation learning from electroencephalography signals. CoRR abs/1911.05419 (2019) - 2018
- [j26]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Faster Independent Component Analysis by Preconditioning With Hessian Approximations. IEEE Trans. Signal Process. 66(15): 4040-4049 (2018) - [c48]Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression. AISTATS 2018: 998-1007 - [c47]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Accelerating Likelihood Optimization for ICA on Real Signals. LVA/ICA 2018: 151-160 - [c46]Jean-Baptiste Schiratti, Jean-Eudes Le Douget, Michel Le Van Quyen, Slim Essid, Alexandre Gramfort:
An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings. ICASSP 2018: 856-860 - [c45]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Faster ICA Under Orthogonal Constraint. ICASSP 2018: 4464-4468 - [c44]Tom Dupré la Tour, Yves Grenier, Alexandre Gramfort:
Driver Estimation in Non-Linear Autoregressive Models. ICASSP 2018: 4519-4523 - [c43]Mathurin Massias, Joseph Salmon, Alexandre Gramfort:
Celer: a Fast Solver for the Lasso with Dual Extrapolation. ICML 2018: 3321-3330 - [c42]Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal, Emmanuel Mignot, Alexandre Gramfort:
A Deep Learning Architecture to Detect Events in EEG Signals During Sleep. MLSP 2018: 1-6 - [c41]Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort:
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals. NeurIPS 2018: 3296-3306 - [c40]Stanislas Chambon, Mathieu N. Galtier, Alexandre Gramfort:
Domain adaptation with optimal transport improves EEG sleep stage classifiers. PRNI 2018: 1-4 - [i30]Hicham Janati, Marco Cuturi, Alexandre Gramfort:
Wasserstein regularization for sparse multi-task regression. CoRR abs/1805.07833 (2018) - [i29]Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort:
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals. CoRR abs/1805.09654 (2018) - [i28]Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis R. Bach:
EM algorithms for ICA. CoRR abs/1805.10054 (2018) - [i27]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Accelerating likelihood optimization for ICA on real signals. CoRR abs/1806.09390 (2018) - [i26]Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal, Emmanuel Mignot, Alexandre Gramfort:
A deep learning architecture to detect events in EEG signals during sleep. CoRR abs/1807.05981 (2018) - [i25]Pierre Ablin, Dylan Fagot, Herwig Wendt, Alexandre Gramfort, Cédric Févotte:
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning. CoRR abs/1811.02225 (2018) - [i24]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Beyond Pham's algorithm for joint diagonalization. CoRR abs/1811.11433 (2018) - [i23]Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal, Emmanuel Mignot, Alexandre Gramfort:
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal. CoRR abs/1812.04079 (2018) - 2017
- [j25]Fabian Pedregosa, Francis R. Bach, Alexandre Gramfort:
On the Consistency of Ordinal Regression Methods. J. Mach. Learn. Res. 18: 55:1-55:35 (2017) - [j24]Eugène Ndiaye, Olivier Fercoq,