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» Better Size Estimation for Sparse Matrix Products
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CORR
2007
Springer
128views Education» more  CORR 2007»
13 years 5 months ago
Model Selection Through Sparse Maximum Likelihood Estimation
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...
ICASSP
2009
IEEE
14 years 17 days ago
Compressive sensing for sparsely excited speech signals
Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse respo...
Thippur V. Sreenivas, W. Bastiaan Kleijn
MST
2010
121views more  MST 2010»
13 years 4 months ago
Entropy of Operators or why Matrix Multiplication is Hard for Depth-Two Circuits
We consider unbounded fanin depth-2 circuits with arbitrary boolean functions as gates. We define the entropy of an operator f : {0, 1}n → {0, 1}m as the logarithm of the maximu...
Stasys Jukna
IJRR
2007
100views more  IJRR 2007»
13 years 5 months ago
Exactly Sparse Extended Information Filters for Feature-based SLAM
Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM sc...
Matthew Walter, Ryan Eustice, John J. Leonard
ISRR
2005
Springer
118views Robotics» more  ISRR 2005»
13 years 11 months ago
A Provably Consistent Method for Imposing Sparsity in Feature-Based SLAM Information Filters
An open problem in Simultaneous Localization and Mapping (SLAM) is the development of algorithms which scale with the size of the environment. A few promising methods exploit the ...
Matthew Walter, Ryan Eustice, John J. Leonard