PDF-Energy Disaggregation via Discriminative Sparse Coding

Zico Kolter Computer Science and Articial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA 02139 koltercsailmitedu Siddarth Batra Andrew Y Ng Computer Science Department Stanford University Stanford CA 94305 sidbatraang css. Quian Quiroga G Kreiman C Koch and I Fried Department of Engineering University of Leicester LE1 7RH Leicester UK Computation and Neural Systems California Institute of Technology 91125 Pasadena CA USA Division of Neurosurgery David Geffen School 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 Temporal disaggregation can be performed with or without one or more high frequency indicator series The package tempdisagg is a collection of several methods for temporal disaggregation Introduction Not having a time series at the desired frequency Zico Kolter Computer Science and Articial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA koltercsailmitedu Matthew J Johnson Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge Lightening talk. Openlab. Major review. 16. th. . Octobre. 2014. Background: the CERN *. aaS. . catalog. . Platform disaggregation. - . 2. OpenStack. LSF. s/w build. …. Interactive. AFS. Ceph. Sparsity. Authors:. Junzhou. Huang, Tong Zhang, . Dimitris. Metaxas. 1. Zhennan Yan. Introduction. Fixed set of . p. basis vectors where for each . j. . --> . Given a random observation , which depends on an underlying coefficient vector .. 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. KH Wong. mean transform v.5a. 1. Introduction. What is object tracking. Track an object in a video, the user gives an initial bounding box. Find the bounding box that cover the target pattern in every frame of the video. Outline. Unusual Event Detection. Video Representation. Dynamic Sparse Coding. Empirical Study. Conclusions. Outline. Unusual Event Detection. Video Representation. Dynamic Sparse Coding. Empirical Study. 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 ∈ . Object Recognition. Murad Megjhani. MATH : 6397. 1. Agenda. Sparse Coding. Dictionary Learning. Problem Formulation (Kernel). Results and Discussions. 2. Motivation. Given a 16x16(or . nxn. ) image . 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 J ZicoKolterRecent Advances in Algorithms for Energy DisaggregationThe GoalDetermine breakdown of power given wholehome consumption eg from smart meterTotal PowerJ ZicoKolterRecent Advances in Algorit