PPT-Sparse Abstract Interpretation

The material in these slides have been taken from the paper Parameterized Construction of Program Representations for Sparse Dataflow Analyses by Tavares et al which is available in the course webpage. Abstract We consider the diameter of a random graph np for various ranges of close to the phase transition point for connectivity For a disconnected graph we use the convention that the diameter of is the maximum diameter of its connected componen 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 illinoisedu kyusvneclabscom Abstract Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data Empirical studies show that mapping the data into a signi64257cantly higher di 27 No 1 pp 2427 Prove rb interpret ation in foren sic evaluations illiam H Campbell MD MBA and A Jocelyn Ritchie JD PhD Prove rb interpretation has long been standar component of the mental status examination and is typica lly elicited during forens From Theory to Practice . Dina . Katabi. O. . Abari. , E. . Adalsteinsson. , A. Adam, F. . adib. , . A. . Agarwal. , . O. C. . Andronesi. , . Arvind. , A. . Chandrakasan. , F. Durand, E. . Hamed. , H. . 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. Full storage:. . 2-dimensional array.. (nrows*ncols) memory.. 31. 0. 53. 0. 59. 0. 41. 26. 0. 31. 41. 59. 26. 53. 1. 3. 2. 3. 1. Sparse storage:. . Compressed storage by columns . (CSC).. Three 1-dimensional arrays.. 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. Tianzhu . Zhang. 1,2. , . Adel Bibi. 1. , . Bernard Ghanem. 1. 1. 2. Circulant. Primal . Formulation. 3. Dual Formulation. Fourier Domain. Time . Domain. Here, the inverse Fourier transform is for each . 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. Author: . Vikas. . Sindhwani. and . Amol. . Ghoting. Presenter: . Jinze. Li. Problem Introduction. we are given a collection of N data points or signals in a high-dimensional space R. D. : xi ∈ . Dina . Katabi. O. . Abari. , E. . Adalsteinsson. , A. Adam, F. . adib. , . A. . Agarwal. , . O. C. . Andronesi. , . Arvind. , A. . Chandrakasan. , F. Durand, E. . Hamed. , H. . Hassanieh. , P. . Indyk. 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 INTerpolation. and . Abstract . interpretation. Arie. . Gurfinkel. (SEI/CMU). with . Aws. . Albarghouthi. and Marsha . Chechik. (U. of Toronto). and . Sagar. . Chaki. (SEI/CMU), and Yi Li (U. of Toronto).