Search Results for author: Russell Poldrack

Found 6 papers, 0 papers with code

NeuroQuery: comprehensive meta-analysis of human brain mapping

no code implementations21 Feb 2020 Jérôme Dockès, Russell Poldrack, Romain Primet, Hande Gözükan, Tal Yarkoni, Fabian Suchanek, Bertrand Thirion, Gaël Varoquaux

Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms.

Text to brain: predicting the spatial distribution of neuroimaging observations from text reports

no code implementations4 Jun 2018 Jérôme Dockès, Demian Wassermann, Russell Poldrack, Fabian Suchanek, Bertrand Thirion, Gaël Varoquaux

In this paper, we propose to mine brain medical publications to learn the spatial distribution associated with anatomical terms.

Information Projection and Approximate Inference for Structured Sparse Variables

no code implementations12 Jul 2016 Rajiv Khanna, Joydeep Ghosh, Russell Poldrack, Oluwasanmi Koyejo

Approximate inference via information projection has been recently introduced as a general-purpose approach for efficient probabilistic inference given sparse variables.

A simple and provable algorithm for sparse diagonal CCA

no code implementations29 May 2016 Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack

Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set, such that the induced canonical variables are maximally correlated.

On Prior Distributions and Approximate Inference for Structured Variables

no code implementations NeurIPS 2014 Oluwasanmi O. Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack

In cases where this projection is intractable, we propose a family of parameterized approximations indexed by subsets of the domain.

BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables

no code implementations NeurIPS 2013 Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep K. Ravikumar, Russell Poldrack

The l1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix even under high-dimensional settings.

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