Concept

Sparse matrix

Related publications (226)

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Gaussian Covariance and Scalable Variational Inference

Matthias Seeger

We analyze computational aspects of variational approximate inference techniques for sparse linear models, which have to be understood to allow for large scale applications. Gaussian covariances play a key role, whose approximation is computationally hard. ...

Omnipress2010

Spread spectrum for compressed sensing techniques in magnetic resonance imaging

Jean-Philippe Thiran, Rolf Gruetter, Pierre Vandergheynst, Dimitri Nestor Alice Van De Ville, Yves Wiaux, Gilles Puy

Magnetic resonance imaging (MRI) probes signals through Fourier measurements. Accelerating the acquisition process is of major interest for various MRI applications. The recent theory of compressed sensing shows that sparse or compressible signals may be r ...

IEEE Signal Process. Soc.2010

Spread Spectrum For Compressed Sensing Techniques In Magnetic Resonance Imaging

Jean-Philippe Thiran, Rolf Gruetter, Pierre Vandergheynst, Dimitri Nestor Alice Van De Ville, Yves Wiaux, Gilles Puy

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Recovery of Compressible Signals in Unions of Subspaces

Volkan Cevher

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals; instead of taking periodic samples, we measure inner products with All < N random vectors and then recover the signal via a sparsity-s ...

2009

A Fast Parallel Matrix Multiplication Reconfigurable Unit Utilized In Face Recognitions Systems

In this paper we present a reconfigurable device which significantly improves the execution time of the most computational intensive functions of three of the most widely used face recognition algorithms; those tasks multiply very large dense matrices. The ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Greedy dictionary selection for sparse representation

Volkan Cevher

We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse, we mean that only a few dictionary elements, compared to the ambient signal ...

2009

Recovery of clustered sparse signals from compressive measurements

Volkan Cevher

We introduce a new signal model, called (K,C)-sparse, to capture K-sparse signals in N dimensions whose nonzero coefficients are contained within at most C clusters, with C < K < N. In contrast to the existing work in the sparse approximation and compress ...

2009

Formalisms for Specifying Markovian Population Models

Barbara Jobstmann, Verena Wolf

We compare several languages for specifying Markovian population models such as queuing networks and chemical reaction networks. These languages -matrix descriptions, stochastic Petri nets, stoichiometric equations, stochastic process algebras, and guarded ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2009

Local bulk S-matrix elements and conformal field theory singularities

We give a procedure for deriving certain bulk S-matrix elements from corresponding boundary correlators. These are computed in the plane-wave limit, via an explicit construction of certain boundary sources that give bulk wave packets. A critical role is pl ...

Amer Physical Soc2009

Sparsity & [and] dictionaries - algorithms & [and] design

Karin Schnass

With the flood of information available today the question how to deal with high dimensional data/signals, which are cumbersome to handle, to calculate with and to store, is highly important. One approach to reducing this flood is to find sparse signal rep ...

EPFL2009