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Locality Sensitive Distributed Computing

Locality Sensitive Distributed Computing. David Peleg Weizmann Institute. Structure of mini-course. Basics of distributed network algorithms Locality-preserving network representations Constructions and applications. Part 2: Representations. Clustered representations

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2.41k views • 230 slides

Sparsity-Based Signal Models and the Sparse K-SVD Algorithm

Sparsity-Based Signal Models and the Sparse K-SVD Algorithm. Ron Rubinstein Advisor: Prof. Michael Elad October 2010. Signal Models. Signal models are a fundamental tool for solving low-level signal processing tasks. Inpainting. Detail enhancement. Deblurring. Tompgraphy reconstruction.

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1.48k views • 83 slides

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Locality Sensitive Distributed Computing

Sparsity-Based Signal Models and the Sparse K-SVD Algorithm