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IRREGULAR
1995
Springer
13 years 11 months ago
Run-Time Techniques for Parallelizing Sparse Matrix Problems
Sparse matrix problems are di cult to parallelize e ciently on message-passing machines, since they access data through multiple levels of indirection. Inspector executor strategie...
Manuel Ujaldon, Shamik D. Sharma, Joel H. Saltz, E...
TIP
2010
162views more  TIP 2010»
13 years 2 months ago
Super-Resolution With Sparse Mixing Estimators
We introduce a class of inverse problem estimators computed by mixing adaptively a family of linear estimators corresponding to different priors. Sparse mixing weights are calcula...
Stéphane Mallat, Guoshen Yu
ICASSP
2011
IEEE
12 years 11 months ago
Computationally efficient regularized acoustic imaging
Sparse recovery techniques have been shown to produce very accurate acoustic images, significantly outperforming traditional deconvolution approaches. However, so far these propo...
Flavio P. Ribeiro, Vitor H. Nascimento
ICASSP
2011
IEEE
12 years 11 months ago
Exploiting sparseness of backing-off language models for efficient look-ahead in LVCSR
In this paper, we propose a new method for computing and applying language model look-ahead in a dynamic network decoder, exploiting the sparseness of backing-off n-gram language ...
David Nolden, Hermann Ney, Ralf Schlüter
SDM
2011
SIAM
370views Data Mining» more  SDM 2011»
12 years 10 months ago
Sparse Latent Semantic Analysis
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The k...
Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G....