PPT-Deformation-Invariant Sparse Coding for Modeling Spatial Va

Author : mitsue-stanley | Published Date : 2016-03-17

George Chen Evelina Fedorenko Nancy Kanwisher Polina Golland 12162011 NIPS MLINI Workshop 2011 1 Talk Outline Finding correspondences between functional regions

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Deformation-Invariant Sparse Coding for Modeling Spatial Va: Transcript

George Chen Evelina Fedorenko Nancy Kanwisher Polina Golland 12162011 NIPS MLINI Workshop 2011 1 Talk Outline Finding correspondences between functional regions in the brain. 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 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 15. 14. A Chessboard Problem. ?. A . Bishop . can only move along a diagonal. Can a . bishop . move from its current position to the question mark?. and calculus of shapes. © Alexander & Michael Bronstein, 2006-2010. tosca.cs.technion.ac.il/book. VIPS Advanced School on. Numerical Geometry of Non-Rigid Shapes . University of Verona, April 2010. 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. 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. Rahul Sharma and Alex Aiken (Stanford University). 1. Randomized Search. x. = . i. ;. y = j;. while . y!=0 . do. . x = x-1;. . y = y-1;. if( . i. ==j ). assert x==0. No!. Yes!. . 2. Invariants. 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. 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. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . Student: Yaniv Tocker. . . Final . Project in 'Introduction to . Computational . & Biological Vision' Course. Motivation. 2. Optical Character Recognition (OCR):. Automatic . translating of letters/digits in images to a form that a computer can manipulate (Strings, ASCII codes. Find a bottle:. 4. Categories. Instances. Find these two objects. Can’t do. unless you do not . care about few errors…. Can nail it. Building a Panorama. M. Brown and D. G. Low. e. . Recognising Panorama. Speaker: Laurent Beauregard laurent.beauregard@isae-supaero.fr. Co-. authors. : Emmanuel . Blazquez. . Dr. St. éphanie. . Lizy-Destrez. 07/06/17. OPTIMIZED TRANSFERS BETWEEN EARTH-MOON INVARIANT MANIFOLDS.

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