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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 hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging
Yousra Bekhti, Felix Lucka, Joseph Salmon +1
Majorization-minimization (MM) is a standard iterative optimization technique which consists in minimizing a sequence of convex surrogate functionals. MM approaches have been parti…
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…
Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy
Laetitia Le, Camille Marini, Alexandre Gramfort +9
Monoclonal antibodies constitute one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors. In order to guar…