PPT-Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns

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 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 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. and Ice Conditions In and Near the Marginal Ice Zone: . The "Marginal Ice Zone Observations and Processes EXperiment" (. MIZOPEX. ). . Goals. : .  . Assess ocean and sea ice variability during the melt season within a key Marginal Ice Zone (MIZ) region. . Matthew Lave and Jan Kleissl. Solar 2011. Variability Reduction through Aggregation. Relative Output. Large ramps are detectable real-time by satellite and ground stations. Variability models primarily needed for large central power plants / . Ph.D. Thesis Defense. Anoop Cherian. *. Department of Computer Science and Engineering. University of Minnesota, Twin-Cities. Adviser. : Prof. Nikolaos Papanikolopoulos. *Contact: . cherian@cs.umn.edu. 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 . Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . Outline. Unusual Event Detection. Video Representation. Dynamic Sparse Coding. Empirical Study. Conclusions. Outline. Unusual Event Detection. Video Representation. Dynamic Sparse Coding. Empirical Study. 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. 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 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. an introduction. Pierre-Louis Toutain. Royal veterinary College London & project officer at the ENV of . Toulouse . Wuhan University October . 2017. To . explain the main concepts related to .