After spending about 5 minutes showing recent results onvideo segmentation (joint work with Adobe), I will describe somerecent works in my group in the area of dictionary learning and sparse coding.In particular I will present new models derived from information theory,new models dedicated to go beyond standard sparse coding applications andinto...
Creator:
Sapiro, Guillermo R. (University of Minnesota, Twin Cities)
Created:
2009-10-06
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
When analyzing large networks, statisticians often assume a generative model in which the observed graph is assumed to come from a stochastic block model, i.e., a random graph with inhomogeneous edge probabilities given in terms of a small block matrix. A non-parametric version of these stochastic block models are so-called W-random graphs, give...
Creator:
Borgs, Christian (Microsoft Research)
Created:
2015-05-19
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We started with a project where we denoised normals to surfaces, then fit the surface to the normals, which we regarded as solving a 4th order PDE via some kind of splitting. This led to remarkably successfulalgorithms for L1 tpe minimizations, constrained and unconstrained. Theseinclude L1, TV, B1,1, nonlocal TV,... Bregman iteration, in its va...
Creator:
Osher, Stanley J. (University of California, Los Angeles)
Created:
2009-03-26
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this talk I will discuss algorithms for factoring polynomials over number fieldsto reach recent results on sparse polynomials, where the complexity of the algorithm takes into account the fact that the polynomial may have many zero coefficients. For simplicity I'll focus on rational polynomials in one or two variables. The talk will review re...
Creator:
Krick, Teresa (University of Buenos Aires)
Created:
2006-09-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
A staggering amount of attention was recently devoted to the studyof compressed sensing and related areas using sparse priors in overparameterized linear models under versions of linear programming (LP) analysis. More recently popularity of the sparsity approach for the classical model ofgroup testing also increased.The threshold phenomenon was ...
Creator:
Malyutov, Mikhail B (Northeastern University)
Created:
2012-02-16
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We introduce and develop a theory of limits for sequences of sparse graphs based on L^p graphons, which generalizes both the existing L^ˆž theory of dense graph limits and its extension by Bollobas and Riordan to sparse graphs without dense spots. In doing so, we replace the no dense spots hypothesis with weaker assumptions, which allow us to an...
Creator:
Chayes, Jennifer (Microsoft Research)
Created:
2015-05-19
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this lecture, we will go beyond Friday's course on structured sparsity, andconsider more complex models. Recently, a large amount of research instatistics and signal processing has been devoted to developing structuredsparse regularization functions. The goal is to encode some a priori knowledgeabout an estimation problem in the regularizatio...
Creator:
Mairal, Julien (INRIA )
Created:
2013-06-22
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Iterative sparse linear solvers are a critical component of a scientific computing platform. Developing effective preconditioning strategies is the main challenge in developing iterative sparse solvers on massively parallel systems. As computing systems become increasingly power-constrained, memory hierarchies for massively parallel systems will...
Creator:
Cohen, Jonathan M. (NVIDIA Corporation)
Created:
2011-01-11
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
