PPT-Nonparametric Scene Parsing:

Label Transfer via Dense Scene Alignment Ce Liu Jenny Yuen Antonio Torralba celiu jenny torralba csailmitedu CSAIL MIT The task of object recognition and scene parsing tree. Freeman Abstract With the advent of the Internet billions of images are now freely available online and constitute a dense sampl ing of the visual world Using a variety of nonparametric metho ds we explore this world with the aid of a large dataset We propose a nonparametric di64256eomorphic image registra tion algorithm based on Thirions demons algorithm The dem ons algo rithm can be seen as an optimization procedure on the entire s pace of displacement 64257elds The main idea of our algorith In this paper we propose a novel nonparametric approach for object recognition and scene parsing using a new technology we name labeltransfer For an input image our system first retrieves its nearest neighbors from a large database containing fully Ling 571. Deep Processing Techniques for NLP. January 12, 2011. Roadmap . Motivation: . Parsing (In) efficiency. Dynamic Programming. Cocke. -. Kasami. -Younger Parsing Algorithm . Chomsky Normal Form. CS 4705. Julia Hirschberg. 1. Some slides adapted from Kathy McKeown and Dan Jurafsky. Syntactic Parsing. Declarative . formalisms like CFGs, FSAs define the . legal strings of a language. -- but only tell you whether a given string is legal in a particular language. Department of Electrical and Computer Engineering. Zhu Han. Department. of Electrical and Computer Engineering. University of Houston.. Thanks to Nam Nguyen. , . Guanbo. . Zheng. , and Dr. . Rong. . --- Functionality, Physics, Causality and Mind. Song-Chun Zhu. University of California, Los Angeles. Scene Understanding Workshop, at CVPR, Portland, Oregon, June 23, 2013. “Dark Matter and Dark Energy”. Richard . Socher. . Cliff . Chiung. -Yu Lin . Andrew Y. Ng . Christopher D. Manning . Slides. . &. . Speech:. . Rui. . Zhang. Outline. Motivation. . &. . Contribution. Recursive. . Neural. . Regression. COSC 878 Doctoral Seminar. Georgetown University. Presenters:. . Sicong Zhang. , . Jiyun. . Luo. .. April. . 1. 4. , 201. 5. 5.0. . Nonparametric Regression. 2. 5.0. . Nonparametric Regression. 1. Some slides . adapted from Julia Hirschberg and Dan . Jurafsky. To view past videos:. http://. globe.cvn.columbia.edu:8080/oncampus.php?c=133ae14752e27fde909fdbd64c06b337. Usually available only for 1 week. Right now, available for all previous lectures. from Streaming Data. Hanzhang. Hu . Daniel Munoz. J. Andrew . Bagnell. Martial Hebert. Scene Parsing. 2. Input. Belief. Propagation. Output. Scene Parsing. 3. Input. f. 1. Output. f. N. …. Inference Machine. CS 4705. Julia Hirschberg. 1. Some slides adapted from Kathy McKeown and Dan Jurafsky. Syntactic Parsing. Declarative . formalisms like CFGs, FSAs define the . legal strings of a language. -- but only tell you whether a given string is legal in a particular language. Cle’ment. . Farabet. , Camille . Couprie. , Laurent . Najman. , and Yann . LeCun. . by Dong Nie. Outline. Background/Motivation. Multiscale. . CNN for feature representation and initial classification. . conditional . VaR. . and . expected shortfall. Outline. Introduction. Nonparametric . Estimators. Statistical . Properties. Application. Introduction. Value-at-risk (. VaR. ) and expected shortfall (ES) are two popular measures of market risk associated with an asset or portfolio of assets..