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» Learning with structured sparsity
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SODA
2012
ACM
209views Algorithms» more  SODA 2012»
11 years 10 months ago
Simple and practical algorithm for sparse Fourier transform
We consider the sparse Fourier transform problem: given a complex vector x of length n, and a parameter k, estimate the k largest (in magnitude) coefficients of the Fourier transf...
Haitham Hassanieh, Piotr Indyk, Dina Katabi, Eric ...
TVLSI
2010
13 years 2 months ago
Fast Analysis of a Large-Scale Inductive Interconnect by Block-Structure-Preserved Macromodeling
To efficiently analyze the large-scale interconnect dominant circuits with inductive couplings (mutual inductances), this paper introduces a new state matrix, called VNA, to stamp ...
Hao Yu, Chunta Chu, Yiyu Shi, David Smart, Lei He,...
SDM
2008
SIAM
140views Data Mining» more  SDM 2008»
13 years 9 months ago
Large-Scale Many-Class Learning
In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. We invest...
Omid Madani, Michael Connor
ECML
2007
Springer
14 years 2 months ago
Bayesian Inference for Sparse Generalized Linear Models
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The paramete...
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg...
ICASSP
2011
IEEE
12 years 11 months ago
USPACOR: Universal sparsity-controlling outlier rejection
The recent upsurge of research toward compressive sampling and parsimonious signal representations hinges on signals being sparse, either naturally, or, after projecting them on a...
Georgios B. Giannakis, Gonzalo Mateos, Shahrokh Fa...