PPT-Random walk

Presented by Changqing Li Mathematics Probability Statistics What is a Random Walk An Intuitive understanding A series of movement which direction and size are randomly decided eg . the Volume of Convex Bodies. By Group 7. The Problem Definition. The main result of the paper is a randomized algorithm for finding an approximation to the volume of a convex body . ĸ. in . n. -dimensional Euclidean space. and Semi-Supervised Learning. Longin Jan Latecki. Based on :. Xiaojin. Zhu. Semi-Supervised Learning with Graphs. PhD thesis. CMU-LTI-05-192, May 2005. Page, Lawrence and . Brin. , Sergey and . Motwani. Richard Peng. M.I.T.. Joint work with . Dehua. Cheng, Yu Cheng, Yan Liu and . Shanghua. . Teng. (U.S.C.). Outline. Gaussian sampling, linear systems, matrix-roots. Sparse factorizations of . L. p. Sungsu. Lim. AALAB, KAIST. Image Segmentation. Computer vision. : make machine to see or to understand/ . interpret . the scenes (images & videos) like human do.. Image segmentation. is one of the most challenging issues in computer vision.. Richard Peng. M.I.T.. OUtline. Structure preserving sampling. Sampling as a recursive ‘driver’. Sampling the inaccessible. What can sampling preserve?. Random Sampling. Collection of many objects. Draft slides. Background. Consider a social graph G=(V, E), where |V|= n and |E|= m . Girvan and Newman’s algorithm for community detection runs . in O(m. 2. n) time. , and . O(n. 2. ) space. .. The . David . Harel. and . Yehuda. . Koren. KDD 2001. Introduction. Advances in database technologies resulted in huge amounts of spatial data. The characteristics of spatial data pose several difficulties for clustering algorithms.. Reflection week beginning 23 May 2016. A Random Act of Kindness. In St Paul’s Cathedral in London, visitors . can climb stairs so as to walk around the inside of the great dome, which is 60 . metres. Richard Peng. M.I.T.. Joint work with . Dehua. Cheng, Yu Cheng, Yan Liu and . Shanghua. . Teng. (U.S.C.). Outline. Gaussian sampling, linear systems, matrix-roots. Sparse factorizations of . L. p. A. pproach. for . R. andom Walk With Restart. on Large Graphs. Kijung Shin. . Jinhong. Jung Lee . Sael. U Kang. Introduction. How can we measure the relevance (or similarity) between two nodes in a graph?. A. pproach. for . R. andom Walk With Restart. on Large Graphs. Kijung Shin. . Jinhong. Jung Lee . Sael. U Kang. Introduction. How can we measure the relevance (or similarity) between two nodes in a graph?. May 17. BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart. 1. Jinhong Jung. Namyong. Park. Lee . Sael. U Kang. Outline. Introduction. Proposed Method. Experiment. Conclusion. “Data is the oil of the new age”. “Data is the oil of the new age”. but, just like oil,. “unrefined data cannot really be used”. “Data is the oil of the new age”. but, just like oil,. Authors: . Kexiang. Wang, . Zhifang. Sui, et al.. Organization: Peking University. Speaker: . Kexiang. Wang. E-mail: wkx@pku.edu.cn. Outline. Overview of Our Paper. Aim. We propose the adjustable affinity-preserving random walk method for generic and query-focused multi-document summarization to enforce the .