PPT-Learning With Dynamic Group Sparsity

Author : stefany-barnette | Published Date : 2017-09-03

Junzhou Huang Xiaolei Huang Dimitris Metaxas Rutgers University Lehigh University Rutgers University Outline Problem Applications where the useful information

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Learning With Dynamic Group Sparsity: Transcript

Junzhou Huang Xiaolei Huang Dimitris Metaxas Rutgers University Lehigh University Rutgers University Outline Problem Applications where the useful information is very less compared with the given data . of Granules Features. Chang Huang and Ram . Nevatia. University of Southern California, Institute for Robotics and Intelligent . Systems. Outline. Introduction. Granules. JRoG. features. Incremental Feature Selection Method. Dynamic Transshipment. &. Evolving Graphs. 2/28/2012. TCS Group Seminar. 1. Seminar outline. 2/28/2012. TCS Group Seminar. 2. Earliest Arrival Flows. Example. 2/28/2012. TCS Group Seminar. 3. S+. 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 .. Sparsity. and Geometry Constrained Dictionary Learning for Action. Recognition from Depth Maps. Jiajia. . Luo. , Wei Wang, and . Hairong. Qi. The University of Tennessee, Knoxville. Presented by: Marwan . Submitted by: Supervised by:. Ankit. . Bhutani. Prof. . Amitabha. . Mukerjee. (Y9227094) Prof. K S . Venkatesh. AUTOENCODERS. AUTO-ASSOCIATIVE NEURAL NETWORKS. OUTPUT SIMILAR AS INPUT. DIMENSIONALITY REDUCTION. . Junzhou. Huang . Xiaolei. Huang . Dimitris. Metaxas . Rutgers University Lehigh University Rutgers University. Outline. Problem: Applications where the useful information is very less compared with the given data . Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . Xun. Jiao. *. , Yu Jiang. +. , . Abbas. . Rahimi. $. , and Rajesh K. Gupta. *. *. University of California, San Diego. +. Tsinghua. University. $. University of California, Berkeley. Agenda. Motivation. sparse acoustic modeling for speech separation. 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. Problem. Segmenting moving . f. oreground in a video. Related work & intuitions. Dynamic background ~ dynamic textures . Image sequences of certain textures moving and changing under certain properties.. ISBA Lecture on Bayesian Foundations June 25. th. 2012. . Bayesian . Dynamic . Modelling. . . Foundations : History of Dynamic Bayes in Action. . How Unthinkable?. Army Operational Knowledge Management. 21 October 2009. Dr. Mark E. Nissen . Center for Edge Power. US Naval Postgraduate School. http://www.nps.edu/Academics/Centers/CEP/. 2. KM Background. st. Half) Unit-II. . Ratna. . Biswas. Assistant Professor. . Vidyasagar. Teachers' Training College. COLLABORATIVE LEARNING. SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks. 9 authors @ NVIDIA, MIT, Berkeley, Stanford. ISCA . 2017. Convolution operation. Reuse. Memory: size vs. access energy. Dataflow decides reuse.

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