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» Sparse Recovery Using Sparse Random Matrices
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CVPR
2010
IEEE
15 years 6 months ago
Fast Matting Using Large Kernel Matting Laplacian Matrices
Image matting is of great importance in both computer vision and graphics applications. Most existing state-of-the-art techniques rely on large sparse matrices such as the matting ...
Kaiming He, Jian Sun, Xiaoou Tang
CISS
2011
IEEE
14 years 1 months ago
Stable manifold embeddings with operators satisfying the Restricted Isometry Property
—Signals of interests can often be thought to come from a low dimensional signal model. The exploitation of this fact has led to many recent interesting advances in signal proces...
Han Lun Yap, Michael B. Wakin, Christopher J. Roze...
60
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CORR
2010
Springer
47views Education» more  CORR 2010»
14 years 8 months ago
Applications of Lindeberg Principle in Communications and Statistical Learning
We use a generalization of the Lindeberg principle developed by Sourav Chatterjee to prove universality properties for various problems in communications, statistical learning and...
Satish Babu Korada, Andrea Montanari
FPGA
2005
ACM
121views FPGA» more  FPGA 2005»
15 years 3 months ago
Floating-point sparse matrix-vector multiply for FPGAs
Large, high density FPGAs with high local distributed memory bandwidth surpass the peak floating-point performance of high-end, general-purpose processors. Microprocessors do not...
Michael DeLorimier, André DeHon
IPPS
1999
IEEE
15 years 2 months ago
Sparse Matrix Block-Cyclic Redistribution
Run-time support for the CYCLIC(k) redistribution on the SPMD computation model is presently very relevant for the scientific community. This work is focused to the characterizati...
Gerardo Bandera, Emilio L. Zapata