63.6k citations · 65.9k across the 85 of their papers we have counts for
6 papers · 2 filters
Faster ICA under orthogonal constraint
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences. In its classical form, ICA relies on m…
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
Stanislas Chambon, Mathieu Galtier, Pierrick Arnal +2
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30s of…
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Mainak Jas, Tom Dupré La Tour, Umut Şimşekli +1
Neural time-series data contain a wide variety of prototypical signal waveforms (atoms) that are of significant importance in clinical and cognitive research. One of the goals for…
Faster independent component analysis by preconditioning with Hessian approximations
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences. In its classic form, ICA relie…
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
Mathurin Massias, Olivier Fercoq, Alexandre Gramfort +1
In high dimension, it is customary to consider Lasso-type estimators to enforce sparsity. For standard Lasso theory to hold, the regularization parameter should be proportional to…
From safe screening rules to working sets for faster Lasso-type solvers
Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Convex sparsity-promoting regularizations are ubiquitous in modern statistical learning. By construction, they yield solutions with few non-zero coefficients, which correspond to s…