1 code implementation • 1 Mar 2022 • Andrew P. Leynes, Nikhil Deveshwar, Srikantan S. Nagarajan, Peder E. Z. Larson
Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling.
1 code implementation • NeurIPS 2021 • Ali Hashemi, Yijing Gao, Chang Cai, Sanjay Ghosh, Klaus-Robert Müller, Srikantan S. Nagarajan, Stefan Haufe
Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models.
no code implementations • NeurIPS 2009 • David P. Wipf, Srikantan S. Nagarajan
Finding maximally sparse representations from overcomplete feature dictionaries frequently involves minimizing a cost function composed of a likelihood (or data fit) term and a prior (or penalty function) that favors sparsity.
no code implementations • NeurIPS 2008 • Julia Owen, Hagai T. Attias, Kensuke Sekihara, Srikantan S. Nagarajan, David P. Wipf
In a restricted setting, the proposed method is shown to have theoretically zero bias estimating both the location and orientation of multi-component dipoles even in the presence of correlations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA.
no code implementations • NeurIPS 2007 • David P. Wipf, Srikantan S. Nagarajan
The result is an efficient algorithm that can be implemented using standard convex programming toolboxes and is guaranteed to converge to a stationary point unlike existing methods.