37 citations · 132 across the 6 of their papers we have counts for
7 papers
Fine-grain atlases of functional modes for fMRI analysis
Kamalaker Dadi, Gaël Varoquaux, Antonia Machlouzarides-Shalit +4
Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data en…
Extracting representations of cognition across neuroimaging studies improves brain decoding
Arthur Mensch, Julien Mairal, Bertrand Thirion +1
Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical…
Learning Neural Representations of Human Cognition across Many fMRI Studies
Arthur Mensch, Julien Mairal, Danilo Bzdok +2
Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets. It opens the door to large-scale statistical models. Finding a unified perspective for…
Stochastic Subsampling for Factorizing Huge Matrices
Arthur Mensch, Julien Mairal, Bertrand Thirion +1
We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns. Learned factors may be sparse or dense and/or non-negative, whi…
Subsampled online matrix factorization with convergence guarantees
Arthur Mensch, Julien Mairal, Gaël Varoquaux +1
We present a matrix factorization algorithm that scales to input matrices that are large in both dimensions (i.e., that contains morethan 1TB of data). The algorithm streams the ma…
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…
Compressed Online Dictionary Learning for Fast fMRI Decomposition
Arthur Mensch, Gaël Varoquaux, Bertrand Thirion
We present a method for fast resting-state fMRI spatial decomposi-tions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning…