11.1k citations · 16.3k across the 88 of their papers we have counts for
6 papers · 1 filter
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Alexandre Abraham, Michael Milham, Adriana Di Martino +4
Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is…
Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signals
Andrés Hoyos-Idrobo, Gaël Varoquaux, Jonas Kahn +1
In this work, we revisit fast dimension reduction approaches, as with random projections and random sampling. Our goal is to summarize the data to decrease computational costs and…
Social-sparsity brain decoders: faster spatial sparsity
Gaël Varoquaux, Matthieu Kowalski, Bertrand Thirion
Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. H…
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Gaël Varoquaux, Pradeep Reddy Raamana, Denis Engemann +3
Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used t…
Dictionary Learning for Massive Matrix Factorization
Arthur Mensch, Julien Mairal, Bertrand Thirion +1
Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising. Its applicability to la…
Learning to Discover Sparse Graphical Models
Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux +1
We consider structure discovery of undirected graphical models from observational data. Inferring likely structures from few examples is a complex task often requiring the formulat…