PDF-Supervised Hashing with Kernels Wei Liu Jun Wang Rongrong Ji YuGang Jiang ShihFu Chang

J Watson Research Center Yorktown Heights NY USA School of Computer Science Fudan University Shanghai China wliurrjisfchang eecolumbiaedu wangjunusibmcom ygjfudaneducn Abstract Recent years have witnessed the growing popularity of hashing in largesc. Looking for the BEST, most competitive pricing for an exciting fishing adventure with an expert guide, quality equipment, and your choice of fishing venue… You’ve found it! Steel Dreams Guide Service offers fishing adventures on a new state of the art 24’ Willie Predator sled, or a peaceful day of drift-fishing through panoramic scenery on a 20’ drift boat. Experience Washington’s premier rivers – the Snake, Columbia or Grande Rondé! Whether your appetite is for salmon, steelhead, small mouth bass or sturgeon – novice or an experienced angler – Steel Dreams is your ultimate guide service. columbiaedu Department of Electrical Engineering Columbia Universit y New York NY 10027 USA Sanjiv Kumar sanjivkgooglecom Google Research New York NY 10011 USA ShihFu Chang sfchangeecolumbiacom Department of Electrical Engineering Columbia Universit Recently bit minwise hashing has been applied to largescale learning and sublinear time near neighbor search The major drawback of minwise hashing is the expensive pre processing as the method requires applying eg 200 to 500 permutations on the dat The analysis uses only very basic and intuitively understandable concepts of probability theory and is meant to be accessible even for undergraduates taking their 64257rst algorithms course 1 Introduction dictionary is a data structure for storing a Up to this point the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically signicant probability that a failure occurs during the insertion of an item requiring an expensive rehashing of all items in th COL 106. Shweta Agrawal, . Amit. Kumar. Slide Courtesy : Linda Shapiro, . Uwash. Douglas W. Harder, . UWaterloo. 12/26/03. Hashing - Lecture 10. 2. The Need for Speed. Data structures we have looked at so far. Yunchao. Gong. UNC Chapel Hill. yunchao@cs.unc.edu. The problem. Large scale image search:. We have a candidate image. Want to search a . large database . to find similar images. Search the . internet. Also known as “meta-data”. April 2016. Comments in SPICE Kernels. 2. Comments, also called “meta-data,” are information that describe the context of kernel data, i.e. “data about data”. Comments are provided inside kernels as plain text (prose). April 2016. Porting Kernels. 2. Porting Issues - 1. Data formats vary across platforms, so data files created on platform “X” may not be usable on platform “Y.”. Binary. . formats. : different platforms use different bit patterns to represent numbers (and possibly characters).. Approximate Near Neighbors. Ilya Razenshteyn (CSAIL MIT). Alexandr. . Andoni. (Simons Institute). Approximate Near Neighbors (ANN). Dataset:. . n. points in . d. dimensions. Query:. a point within . and . Distributional. Semantics. :. Between. . Syntactic. . Structures. and . Compositional. . Distributional. Semantics. Fabio Massimo . Zanzotto. ART Group. Dipartimento di Ingegneria dell’Impresa. Kai Li, Guo-Jun Qi, Jun Ye, Tuoerhongjiang Yusuph, Kien A. Hua. Department of Computer Science. University of Central Florida. ISM 2016. Presented by . Tuoerhongjiang Yusuph. Introduction. Massive amount of high-dimensional data, high computational costs …. 1LOTHACHIRO -AEkhao1a Ntsinran to ephyo ji na tae na ete ekm ji benritokcho mektokhatolia benritokchothakchoesa kmtokala Ete na heto phyokhoka nonghori jiang ntsinranji ethev lia tolankawoe na tae mek –. Imitation Learning in NLP. Kai-Wei Chang. CS @ . UCLA. kw@kwchang.net. Couse webpage: . https://uclanlp.github.io/CS269-17. /. 1. ML in NLP. Learning to search . approaches. Shift-Reduce parser.