PDF-Static Single Assignment SSA Form A sparse program representation for dataow

CSL862 SSA form brPage 2br Computing Static Single Assignment SSA Form Overview What is SSA Advantages of SSA over usedef chains Flavors of SSA Dominance frontiers Inserting nodes Renaming the temporaries Translating out of SSA form R Cytron J Ferra. Such matrices has several attractive properties they support algorithms with low computational complexity and make it easy to perform in cremental updates to signals We discuss applications to several areas including compressive sensing data stream Volkan . Cevher. volkan.cevher@epfl.ch. Laboratory. for Information . . and Inference Systems - . LIONS. . http://lions.epfl.ch. Linear Dimensionality Reduction. Compressive sensing. non-adaptive measurements. Raja . Giryes. ICASSP 2011. Volkan. Cevher. Agenda. The sparse approximation problem. Algorithms and pre-run guarantees. Online performance guarantees. Performance bound. Parameter selection. 2. Sparse approximation. to Multiple Correspondence . Analysis. G. Saporta. 1. , . A. . . Bernard. 1,2. , . C. . . Guinot. 2,3. 1 . CNAM, Paris, France. 2 . CE.R.I.E.S., Neuilly sur Seine, France. 3 . Université. . François Rabelais. Recovery. . (. Using . Sparse. . Matrices). Piotr. . Indyk. MIT. Heavy Hitters. Also called frequent elements and elephants. Define. HH. p. φ. . (. x. ) = { . i. : |x. i. | ≥ . φ. ||. x||. p. . Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. MS45: Recent Advances in Sparse and . Non-local Image Regularization - Part III of III. onto convex sets. Volkan. Cevher. Laboratory. for Information . . and Inference Systems – . LIONS / EPFL. http://lions.epfl.ch . . joint work with . Stephen Becker. Anastasios. . Kyrillidis. ISMP’12. Weihong Deng (. 邓伟洪. ). Beijing Univ. Post. & Telecom.(. 北京邮电大学. ) . 2. Characteristics of Face Pattern. The facial shapes are too similar, sometimes identical ! (~100% face detection rate, kinship verification). Ron Rubinstein. Advisor: Prof. Michael . Elad. October 2010. Signal Models. Signal models. . are a fundamental tool for solving low-level signal processing tasks. Noise Removal. Image Scaling. Compression. . Jeremy Watt and . Aggelos. . Katsaggelos. Northwestern University. Department of EECS. Part 2: Quick and dirty optimization techniques. Big picture – a story of 2’s. 2 excellent greedy algorithms: . Contents. Problem Statement. Motivation. Types . of . Algorithms. Sparse . Matrices. Methods to solve Sparse Matrices. Problem Statement. Problem Statement. The . solution . of . the linear system is the values of the unknown vector . Yi Ma. 1,2. . Allen Yang. 3. John . Wright. 1. CVPR Tutorial, June 20, 2009. 1. Microsoft Research Asia. 3. University of California Berkeley. 2. University of Illinois . at Urbana-Champaign. Chaoqiang Deng (NYU) . and . Kedar. . Namjoshi. (Bell Labs, Nokia). NOKIA . Bell Labs. Correctness ≠ Security. Case in point: static single assignment (SSA). . Chaoqiang Deng | New York University | 08/30/2017. Parallelization of Sparse Coding & Dictionary Learning Univeristy of Colorado Denver Parallel Distributed System Fall 2016 Huynh Manh 11/15/2016 1 Contents Introduction to Sparse Coding Applications of Sparse Representation