
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals
Convolutional dictionary learning (CDL) estimates shift invariant basis ...
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Beyond Pham's algorithm for joint diagonalization
The approximate joint diagonalization of a set of matrices consists in f...
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Selfsupervised representation learning from electroencephalography signals
The supervised learning paradigm is limited by the cost  and sometimes ...
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Support recovery and supnorm convergence rates for sparse pivotal estimation
In high dimensional sparse regression, pivotal estimators are estimators...
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Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise
Sparsity promoting norms are frequently used in high dimensional regress...
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SpatioTemporal Alignments: Optimal transport through space and time
Comparing data defined over space and time is notoriously hard, because ...
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Multisubject MEG/EEG source imaging with sparse multitask regression
Magnetoencephalography and electroencephalography (M/EEG) are noninvasi...
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Dual Extrapolation for Sparse Generalized Linear Models
Generalized Linear Models (GLM) form a wide class of regression and clas...
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Learning step sizes for unfolded sparse coding
Sparse coding is typically solved by iterative optimization techniques, ...
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Implicit differentiation of Lassotype models for hyperparameter optimization
Setting regularization parameters for Lassotype estimators is notorious...
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Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
Magnetoencephalography (MEG) and electroencephalography (EEG) are noni...
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Accelerating likelihood optimization for ICA on real signals
We study optimization methods for solving the maximum likelihood formula...
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Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Frequencyspecific patterns of neural activity are traditionally interpr...
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A QuasiNewton algorithm on the orthogonal manifold for NMF with transform learning
Nonnegative matrix factorization (NMF) is a popular method for audio spe...
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Manifoldregression to predict from MEG/EEG brain signals without source modeling
Magnetoencephalography and electroencephalography (M/EEG) can reveal neu...
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Faster ICA under orthogonal constraint
Independent Component Analysis (ICA) is a technique for unsupervised exp...
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Faster independent component analysis by preconditioning with Hessian approximations
Independent Component Analysis (ICA) is a technique for unsupervised exp...
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Generalized Concomitant MultiTask Lasso for sparse multimodal regression
In high dimension, it is customary to consider Lassotype estimators to ...
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Learning the Morphology of Brain Signals Using AlphaStable Convolutional Sparse Coding
Neural timeseries data contain a wide variety of prototypical signal wa...
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A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
Sleep stage classification constitutes an important preliminary exam in ...
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From safe screening rules to working sets for faster Lassotype solvers
Convex sparsitypromoting regularizations are ubiquitous in modern stati...
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Gap Safe screening rules for sparsity enforcing penalties
In high dimensional regression settings, sparsity enforcing penalties ha...
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The iterative reweighted MixedNorm Estimate for spatiotemporal MEG/EEG source reconstruction
Source imaging based on magnetoencephalography (MEG) and electroencephal...
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Anomaly Detection and Localisation using Mixed Graphical Models
We propose a method that performs anomaly detection and localisation wit...
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Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
In high dimensional settings, sparse structures are crucial for efficien...
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GAP Safe Screening Rules for SparseGroupLasso
In high dimensional settings, sparse structures are crucial for efficien...
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Calibration of OneClass SVM for MV set estimation
A general approach for anomaly detection or novelty detection consists i...
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GAP Safe screening rules for sparse multitask and multiclass models
High dimensional regression benefits from sparsity promoting regularizat...
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Mind the duality gap: safer rules for the Lasso
Screening rules allow to early discard irrelevant variables from the opt...
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JitterAdaptive Dictionary Learning  Application to MultiTrial Neuroelectric Signals
Dictionary Learning has proven to be a powerful tool for many image proc...
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Fast Optimal Transport Averaging of Neuroimaging Data
Knowing how the Human brain is anatomically and functionally organized a...
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Improved brain pattern recovery through ranking approaches
Inferring the functional specificity of brain regions from functional Ma...
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Machine Learning for Neuroimaging with ScikitLearn
Statistical machine learning methods are increasingly used for neuroimag...
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Multiscale Mining of fMRI data with Hierarchical Structured Sparsity
Inverse inference, or "brain reading", is a recent paradigm for analyzin...
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Brain covariance selection: better individual functional connectivity models using population prior
Spontaneous brain activity, as observed in functional neuroimaging, has ...
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Second order scattering descriptors predict fMRI activity due to visual textures
Second layer scattering descriptors are known to provide good classifica...
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Learning to rank from medical imaging data
Medical images can be used to predict a clinical score coding for the se...
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Smallsample Brain Mapping: Sparse Recovery on Spatially Correlated Designs with Randomization and Clustering
Functional neuroimaging can measure the brain?s response to an external ...
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A supervised clustering approach for fMRIbased inference of brain states
We propose a method that combines signals from many brain regions observ...
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Total variation regularization for fMRIbased prediction of behaviour
While medical imaging typically provides massive amounts of data, the ex...
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A hierarchical Bayesian perspective on majorizationminimization for nonconvex sparse regression: application to M/EEG source imaging
Majorizationminimization (MM) is a standard iterative optimization tech...
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Wasserstein regularization for sparse multitask regression
Two important elements have driven recent innovation in the field of reg...
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A deep learning architecture to detect events in EEG signals during sleep
Electroencephalography (EEG) during sleep is used by clinicians to evalu...
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EM algorithms for ICA
Independent component analysis (ICA) is a widely spread data exploration...
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DOSED: a deep learning approach to detect multiple sleep microevents in EEG signal
Background: Electroencephalography (EEG) monitors brain activity during ...
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Alexandre Gramfort
verfied profile
Senior researcher in machine learning and signal processing at Inria since 2017, Assistant Professor at Telecom ParisTech from 20122017, Research Fellow at Harvard University / Massachusetts General Hospital from 20102011, Research fellow at INRIA from 20092010. Graduated from Ecole Polytechnique, France.