Alexandre Gramfort

Orcid: 0000-0001-9791-4404

According to our database1, Alexandre Gramfort authored at least 240 papers between 2009 and 2024.

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Bibliography

2024
Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference.
PLoS Comput. Biol., February, 2024

Local linear convergence of proximal coordinate descent algorithm.
Optim. Lett., January, 2024

emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography.
CoRR, 2024

Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment.
CoRR, 2024

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation.
CoRR, 2024

Geodesic Optimization for Predictive Shift Adaptation on EEG data.
CoRR, 2024

Diffusion posterior sampling for simulation-based inference in tall data settings.
CoRR, 2024

Cycling on the Freeway: The Perilous State of Open Source Neuroscience Software.
CoRR, 2024

Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications.
CoRR, 2024

Physics-Informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets.
Proceedings of the 32nd European Signal Processing Conference, 2024

2023

nipy/nibabel: 5.2.0.
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Dataset, December, 2023

bids-specification.
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Dataset, November, 2023

nilearn.
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Dataset, October, 2023

nipy/nibabel: 5.1.0.
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Dataset, April, 2023

nipy/nibabel: 5.0.1.
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Dataset, February, 2023

nipy/nibabel: 5.0.0.
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Dataset, January, 2023

Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset.
NeuroImage, 2023

Evaluating the structure of cognitive tasks with transfer learning.
CoRR, 2023

Convolutional Monge Mapping Normalization for learning on biosignals.
CoRR, 2023

Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation.
CoRR, 2023

L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Convolution Monge Mapping Normalization for learning on sleep data.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multiview Independent Component Analysis with Delays.
Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels.
Proceedings of the International Conference on Machine Learning, 2023

2022



nipy/nipype: 1.8.3.
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Dataset, July, 2022


nipy/nibabel.
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Dataset, June, 2022

nipy/nibabel: 4.0.0rc0.
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Dataset, June, 2022

nipy/nibabel: 3.2.2.
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Dataset, June, 2022

nipy/nipype: 1.8.1.
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Dataset, May, 2022

nipy/nipype: 1.8.0.
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Dataset, May, 2022

nipy/nipype: 1.7.1.
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Dataset, April, 2022


Intracranial Electrode Location and Analysis in MNE-Python.
Dataset, February, 2022

DiCoDiLe: Distributed Convolutional Dictionary Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

A unified view on beamformers for M/EEG source reconstruction.
NeuroImage, 2022

A reusable benchmark of brain-age prediction from M/EEG resting-state signals.
NeuroImage, 2022

Robust learning from corrupted EEG with dynamic spatial filtering.
NeuroImage, 2022

Intracranial Electrode Location and Analysis in MNE-Python.
J. Open Source Softw., 2022

Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning.
J. Mach. Learn. Res., 2022

Validation Diagnostics for SBI algorithms based on Normalizing Flows.
CoRR, 2022

Data augmentation for learning predictive models on EEG: a systematic comparison.
CoRR, 2022

Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments.
CoRR, 2022

The optimal noise in noise-contrastive learning is not what you think.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Deep invariant networks with differentiable augmentation layers.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Toward a realistic model of speech processing in the brain with self-supervised learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Benchopt: Reproducible, efficient and collaborative optimization benchmarks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals.
Proceedings of the Tenth International Conference on Learning Representations, 2022

DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals.
Proceedings of the Tenth International Conference on Learning Representations, 2022

LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso.
Proceedings of the International Conference on Automated Machine Learning, 2022

2021

nipy/nipype: 1.7.0.
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Dataset, October, 2021



mvlearn: Multiview Machine Learning in Python.
J. Mach. Learn. Res., 2021

POT: Python Optimal Transport.
J. Mach. Learn. Res., 2021

DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals.
CoRR, 2021

Long-range and hierarchical language predictions in brains and algorithms.
CoRR, 2021

Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements.
CoRR, 2021

Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction.
CoRR, 2021

CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals.
CoRR, 2021

Deep Recurrent Encoder: A scalable end-to-end network to model brain signals.
CoRR, 2021

Decomposing lexical and compositional syntax and semantics with deep language models.
CoRR, 2021

Adaptive Multi-View ICA: Estimation of noise levels for optimal inference.
CoRR, 2021

Leveraging Global Parameters for Flow-based Neural Posterior Estimation.
CoRR, 2021


HNPE: Leveraging Global Parameters for Neural Posterior Estimation.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Shared Independent Component Analysis for Multi-Subject Neuroimaging.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Cytoarchitecture Measurements in Brain Gray Matter Using Likelihood-Free Inference.
Proceedings of the Information Processing in Medical Imaging, 2021

Disentangling syntax and semantics in the brain with deep networks.
Proceedings of the 38th International Conference on Machine Learning, 2021

Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

Learning with self-supervision on EEG data.
Proceedings of the 9th International Winter Conference on Brain-Computer Interface, 2021

2020

nipy/nibabel: 3.2.1.
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Dataset, November, 2020

nipy/nibabel: 3.2.0.
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Dataset, October, 2020


nipy/nipype: 1.5.1.
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Dataset, September, 2020

nipy/nipype: 1.5.0.
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Dataset, June, 2020

nipy/nibabel: 3.1.1.
Dataset, June, 2020

nipy/nibabel: 3.1.0.
Dataset, April, 2020



nipy/nibabel: 3.0.2.
Dataset, March, 2020

nipy/nipype: 1.4.2.
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Dataset, February, 2020

nipy/nipype: 1.4.1.
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Dataset, January, 2020

nipy/nibabel: 3.0.1.
Dataset, January, 2020

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium.
npj Digit. Medicine, 2020

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.
NeuroImage, 2020

Multi-subject MEG/EEG source imaging with sparse multi-task regression.
NeuroImage, 2020

Comparison of beamformer implementations for MEG source localization.
NeuroImage, 2020

Learning summary features of time series for likelihood free inference.
CoRR, 2020

Model identification and local linear convergence of coordinate descent.
CoRR, 2020

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso.
CoRR, 2020

Uncovering the structure of clinical EEG signals with self-supervised learning.
CoRR, 2020

Modeling Shared responses in Neuroimaging Studies through MultiView ICA.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Debiased Sinkhorn barycenters.
Proceedings of the 37th International Conference on Machine Learning, 2020

Implicit differentiation of Lasso-type models for hyperparameter optimization.
Proceedings of the 37th International Conference on Machine Learning, 2020

Support recovery and sup-norm convergence rates for sparse pivotal estimation.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Spatio-temporal alignments: Optimal transport through space and time.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
nipy/nipype: 1.4.0.
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Dataset, December, 2019




nipy/nipype: 1.3.0.
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Dataset, November, 2019


nipy/nipype: 1.3.0-rc1.
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Dataset, October, 2019

nipy/nipype: 1.2.2.
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Dataset, September, 2019

nipy/nipype: 1.2.2.
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Dataset, September, 2019

nipy/nipype: 1.2.3.
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Dataset, September, 2019


nipy/nipype: 1.2.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, August, 2019


nipy/nipype: 1.2.0.
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Dataset, May, 2019



nipy/nipype: 1.1.9.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, February, 2019

nipy/nipype: 1.1.8.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, January, 2019

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis.
J. Open Source Softw., 2019

Dual Extrapolation for Sparse Generalized Linear Models.
CoRR, 2019

Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise.
CoRR, 2019

Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals.
CoRR, 2019

Deep learning-based electroencephalography analysis: a systematic review.
CoRR, 2019

Manifold-regression to predict from MEG/EEG brain signals without source modeling.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning step sizes for unfolded sparse coding.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Self-Supervised Representation Learning from Electroencephalography Signals.
Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, 2019

Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates.
Proceedings of the Information Processing in Medical Imaging, 2019

A Quasi-Newton Algorithm on the Orthogonal Manifold for NMF with Transform Learning.
Proceedings of the IEEE International Conference on Acoustics, 2019

Beyond Pham's algorithm for joint diagonalization.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Wasserstein regularization for sparse multi-task regression.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Stochastic algorithms with descent guarantees for ICA.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
nipy/nipype: 1.1.7.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, December, 2018

nipy/nipype: 1.1.6.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2018

nipy/nipype: 1.1.5.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2018

nipy/nipype: 1.1.4.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, October, 2018

nipy/nipype: 1.1.3.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, September, 2018

nipy/nipype: 1.1.2.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, August, 2018

nipy/nipype: 1.1.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, July, 2018

nipy/nipype: Nipype 1.1.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, July, 2018

nipy/nipype: 1.0.4.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, May, 2018

nipy/nipype: 1.0.4.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, May, 2018

nipy/nipype: Nipype 1.0.3.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, April, 2018

nipy/nipype: 1.0.2.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, March, 2018

nipy/nipype: 1.0.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, February, 2018

nipy/nipype: Nipype - v1.0.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, January, 2018

Faster Independent Component Analysis by Preconditioning With Hessian Approximations.
IEEE Trans. Signal Process., 2018

DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal.
CoRR, 2018

EM algorithms for ICA.
CoRR, 2018

Domain adaptation with optimal transport improves EEG sleep stage classifiers.
Proceedings of the 2018 International Workshop on Pattern Recognition in Neuroimaging, 2018

Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Deep Learning Architecture to Detect Events in EEG Signals During Sleep.
Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018

Celer: a Fast Solver for the Lasso with Dual Extrapolation.
Proceedings of the 35th International Conference on Machine Learning, 2018

Driver Estimation in Non-Linear Autoregressive Models.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Faster ICA Under Orthogonal Constraint.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Accelerating Likelihood Optimization for ICA on Real Signals.
Proceedings of the Latent Variable Analysis and Signal Separation, 2018

Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
nipy/nipype: 0.14.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2017

nipy/nipype: 0.14.0-rc1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2017

nipy/nipype: 0.14.0-rc1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2017

Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. 0.13.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, May, 2017


Non-linear auto-regressive models for cross-frequency coupling in neural time series.
PLoS Comput. Biol., 2017

Autoreject: Automated artifact rejection for MEG and EEG data.
NeuroImage, 2017

Seeing it all: Convolutional network layers map the function of the human visual system.
NeuroImage, 2017

On the Consistency of Ordinal Regression Methods.
J. Mach. Learn. Res., 2017

Gap Safe Screening Rules for Sparsity Enforcing Penalties.
J. Mach. Learn. Res., 2017

Convolutional Network Layers Map the Function of the Human Visual Cortex.
ERCIM News, 2017

From safe screening rules to working sets for faster Lasso-type solvers.
CoRR, 2017

Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy.
CoRR, 2017

A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series.
CoRR, 2017

Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Parametric estimation of spectrum driven by an exogenous signal.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Caveats with Stochastic Gradient and Maximum Likelihood Based ICA for EEG.
Proceedings of the Latent Variable Analysis and Signal Separation, 2017

Hyperparameter estimation in maximum a posteriori regression using group sparsity with an application to brain imaging.
Proceedings of the 25th European Signal Processing Conference, 2017

Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction.
IEEE Trans. Medical Imaging, 2016

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression.
CoRR, 2016

Automated rejection and repair of bad trials in MEG/EEG.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2016

M/EEG source localization with multi-scale time-frequency dictionaries.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2016

GAP Safe Screening Rules for Sparse-Group Lasso.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Data-driven HRF estimation for encoding and decoding models.
NeuroImage, 2015

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.
NeuroImage, 2015

MEG/EEG Source Imaging with a Non-Convex Penalty in the Time-Frequency Domain.
Proceedings of the 2015 International Workshop on Pattern Recognition in NeuroImaging, 2015

Mind the Noise Covariance When Localizing Brain Sources with M/EEG.
Proceedings of the 2015 International Workshop on Pattern Recognition in NeuroImaging, 2015

GAP Safe screening rules for sparse multi-task and multi-class models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast Optimal Transport Averaging of Neuroimaging Data.
Proceedings of the Information Processing in Medical Imaging, 2015

Mind the duality gap: safer rules for the Lasso.
Proceedings of the 32nd International Conference on Machine Learning, 2015

FAμST: Speeding up linear transforms for tractable inverse problems.
Proceedings of the 23rd European Signal Processing Conference, 2015

Inverse problems with time-frequency dictionaries and non-white Gaussian noise.
Proceedings of the 23rd European Signal Processing Conference, 2015

Calibration of One-Class SVM for MV set estimation.
Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015

2014
Blind Denoising with Random Greedy Pursuits.
IEEE Signal Process. Lett., 2014

Supramodal processing optimizes visual perceptual learning and plasticity.
NeuroImage, 2014

Encoding of event timing in the phase of neural oscillations.
NeuroImage, 2014

MNE software for processing MEG and EEG data.
NeuroImage, 2014

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning.
Medical Image Anal., 2014

Machine learning for neuroimaging with scikit-learn.
Frontiers Neuroinformatics, 2014

Improved MEG/EEG source localization with reweighted mixed-norms.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

Benchmarking solvers for TV-ℓ1 least-squares and logistic regression in brain imaging.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

Decoding perceptual thresholds from MEG/EEG.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

2013
Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness.
NeuroImage, 2013

Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations.
NeuroImage, 2013

Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals
Proceedings of the 1st International Conference on Learning Representations, 2013

API design for machine learning software: experiences from the scikit-learn project.
CoRR, 2013

HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

Identifying Predictive Regions from fMRI with TV-L1 Prior.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

Second Order Scattering Descriptors Predict fMRI Activity Due to Visual Textures.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

Non-negative Tensor Factorization for single-channel EEG artifact rejection.
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2013

Learning from M/EEG Data with Variable Brain Activation Delays.
Proceedings of the Information Processing in Medical Imaging, 2013

Non-negative matrix factorization for single-channel EEG artifact rejection.
Proceedings of the IEEE International Conference on Acoustics, 2013

2012
Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity.
SIAM J. Imaging Sci., 2012

A supervised clustering approach for fMRI-based inference of brain states.
Pattern Recognit., 2012

Improved Brain Pattern Recovery through Ranking Approaches.
Proceedings of the Second International Workshop on Pattern Recognition in NeuroImaging, 2012

Decoding Visual Percepts Induced by Word Reading with fMRI.
Proceedings of the Second International Workshop on Pattern Recognition in NeuroImaging, 2012

Multilayer Scattering Image Analysis Fits fMRI Activity in Visual Areas.
Proceedings of the Second International Workshop on Pattern Recognition in NeuroImaging, 2012

Learning to Rank from Medical Imaging Data.
Proceedings of the Machine Learning in Medical Imaging - Third International Workshop, 2012

Sparse DSI: Learning DSI Structure for Denoising and Fast Imaging.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, 2012

Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Total Variation Regularization for fMRI-Based Prediction of Behavior.
IEEE Trans. Medical Imaging, 2011

Tracking cortical activity from M/EEG using graph cuts with spatiotemporal constraints.
NeuroImage, 2011

Phase delays within visual cortex shape the response to steady-state visual stimulation.
NeuroImage, 2011

Scikit-learn: Machine Learning in Python.
J. Mach. Learn. Res., 2011

Total variation regularization for fMRI-based prediction of behaviour
CoRR, 2011

Forward Field Computation with OpenMEEG.
Comput. Intell. Neurosci., 2011

Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity.
Proceedings of the 2011 International Workshop on Pattern Recognition in NeuroImaging, 2011

A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding.
Proceedings of the Machine Learning and Interpretation in Neuroimaging, 2011

Beyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and Identify.
Proceedings of the Machine Learning and Interpretation in Neuroimaging, 2011

Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity.
Proceedings of the Information Processing in Medical Imaging, 2011

Functional Brain Imaging with M/EEG Using Structured Sparsity in Time-Frequency Dictionaries.
Proceedings of the Information Processing in Medical Imaging, 2011

2010
A priori par normes mixtes pour les problèmes inverses. Application à la localisation de sources en M/EEG.
Traitement du Signal, 2010

Graph-Based Variability Estimation in Single-Trial Event-Related Neural Responses.
IEEE Trans. Biomed. Eng., 2010

Brain covariance selection: better individual functional connectivity models using population prior.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009
Mapping, timing and tracking cortical activations with MEG and EEG: Methods and application to human vision. (Localisation et suivi d'activité fonctionnelle cérébrale en électro et magnétoencéphalographie: Méthodes et applications au système visuel humain).
PhD thesis, 2009

Improving M/EEG Source Localization with an Inter-Condition Sparse Prior.
Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28, 2009


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