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» On Wiedemann's Method of Solving Sparse Linear Systems
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SIGPRO
2010
135views more  SIGPRO 2010»
13 years 7 months ago
A short note on compressed sensing with partially known signal support
This short note studies a variation of the Compressed Sensing paradigm introduced recently by Vaswani et al., i.e. the recovery of sparse signals from a certain number of linear m...
Laurent Jacques
TIP
2011
123views more  TIP 2011»
13 years 4 months ago
Spatial Sparsity-Induced Prediction (SIP) for Images and Video: A Simple Way to Reject Structured Interference
We propose a prediction technique that is geared toward forming successful estimates of a signal based on a correlated anchor signal that is contaminated with complex interference...
Gang Hua, Onur G. Guleryuz
CONSTRAINTS
2008
89views more  CONSTRAINTS 2008»
13 years 9 months ago
A Reinforcement Learning Approach to Interval Constraint Propagation
When solving systems of nonlinear equations with interval constraint methods, it has often been observed that many calls to contracting operators do not participate actively to th...
Frédéric Goualard, Christophe Jerman...
ECCV
2002
Springer
14 years 11 months ago
Linear Multi View Reconstruction with Missing Data
General multi view reconstruction from affine or projective cameras has so far been solved most efficiently using methods of factorizing image data matrices into camera and scene p...
Carsten Rother, Stefan Carlsson
TNN
1998
100views more  TNN 1998»
13 years 8 months ago
A dynamical system perspective of structural learning with forgetting
—Structural learning with forgetting is an established method of using Laplace regularization to generate skeletal artificial neural networks. In this paper we develop a continu...
D. A. Miller, J. M. Zurada