Campuses:

sparsity

Monday, October 14, 2019 - 2:55pm - 3:45pm
Mariya Doneva (Philips Research Laboratory)
This lecture gives an overview of methods for scan time reduction in quantitative MRI based on regularized image reconstruction. Besides the generic constraints that can be used for image series, the known signal model in quantitative MRI permits designing a model-based constraint tailored to the specific application. This is a much stronger prior knowledge, which, provided that the model is accurate, enables even higher accelerations and improved image quality.
Wednesday, April 25, 2018 - 3:30pm - 4:00pm
Christopher Rozell (Georgia Institute of Technology)
Tracking time-varying signals is an important part of forecasting in complex time-series data. Recently, signal processing techniques have been developed to improve tracking performance when the signal of interest is known a-priori to be sparse. In this talk we will review a collection of related algorithms we have developed for dynamic filtering of time-varying sparse signals. The foundations of this work are based on two algorithms that leverage efficient L1 optimization methods.
Tuesday, January 26, 2016 - 2:00pm - 2:50pm
Caroline Uhler (Massachusetts Institute of Technology)
We discuss properties of distributions that are multivariate totally positive of order two (MTP2). Such distributions appear in the context of positive dependence, ferromagnetism in the Ising model, and various latent models. We show that maximum likelihood estimation for MPT2 exponential families is a convex problem. Hence, if the MLE exists, it is unique. In the Gaussian setting we prove that the MLE exists with only 2 observations and that MTP2 implies sparsity of the concentration matrix without the need of a tuning parameter.
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