PDF-Sparse deep belief net model for visual area V Honglak

Ng Computer Science Department Stanford University Stanford CA 94305 hlleechaituang csstanfordedu Abstract Motivated in part by the hierarchical organization of the cortex a number of al gorithms have recently been proposed that try to learn hierarc. Ng Computer Science Department Stanford University Stanford CA 94305 hlleechaituang csstanfordedu Abstract Motivated in part by the hierarchical organization of the cortex a number of al gorithms have recently been proposed that try to learn hierarc Ng Computer Science Department Stanford University Stanford CA 94305 Abstract Sparse coding provides a class of algorithms for 64257nding succinct representations of stimuli given only unlabeled input data it discovers basis functions that cap ture 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. Aswin C Sankaranarayanan. Rice University. Richard G. . Baraniuk. Andrew E. Waters. Background subtraction in surveillance videos. s. tatic camera with foreground objects. r. ank 1 . background. s. parse. 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. Aditya. Chopra and Prof. Brian L. Evans. Department of Electrical and Computer Engineering. The University of Texas at Austin. 1. Introduction. Finite Impulse Response (FIR) model of transmission media. 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. 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. . 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. Edubull provides online Dot Net Course. Dot Net training includes .Net Curriculum, Visual .Net, dot Net Basics, Framework, along with Online learning app, dot net framework and Asp Dot Net Video Tutorials Afsaneh . Asaei. Joint work with: . Mohammad . Golbabaee. ,. Herve. Bourlard, . Volkan. . Cevher. φ. 21. φ. 52. s. 1. s. 2. s. 3. . s. 4. s. 5. x. 1. x. 2. φ. 11. φ. 42. 2. Speech . Separation Problem.