'Random walks' presentation slideshows

Random walks, eigenvectors, and their applications to Information Retrieval, Natural Language Processing, and Machine Le

Random walks, eigenvectors, and their applications to Information Retrieval, Natural Language Processing, and Machine Learning. Dragomir R. Radev University of Michigan radev@umich.edu Guest lecture in SI 614 March 7, 2006. INTRODUCTION. Social networks. Induced by a relation

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Unexpected drop of dynamical heterogeneities in colloidal suspensions approaching the jamming transition

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PRICE DISCOVERY IN SPOT and FUTURE HARD COMMODITY MARKETS

871 views • 55 slides

Quantum Random Walks

Combinatorial and Computational Aspects of Statistical Physics/ Random Graphs and Structures Cambridge, September 5, 2002. Quantum Random Walks. Julia Kempe Computer Science Division and Department of Chemistry, University of California, Berkeley &

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Link Analysis Ranking

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Rumors , consensus and epidemics on networks

Rumors , consensus and epidemics on networks. J. Ganesh University of Bristol. Rumor spreading. Population of size n One person knows a rumor at time 0 Time is discrete In each time step, each person who knows the rumor chooses another person at random and informs them of it

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Never Ending Language Learning Carnegie Mellon University

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CSE 291 Fall 2001 Marina Meila and Jianbo Shi: Learning Segmentation by Random Walks/A Random Walks View of Spectral Seg

Markus Herrgard UCSD Bioengineering and Bioinformatics. CSE 291 Fall 2001 Marina Meila and Jianbo Shi: Learning Segmentation by Random Walks/A Random Walks View of Spectral Segmentation. Overview. Introduction: Why random walks? Review of the Ncut algorithm Finite Markov chains

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Link Analysis

Link Analysis. Objectives. To review common approaches to link analysis To calculate the popularity of a site based on link analysis To model human judgments indirectly. Outline. Early Approaches to Link Analysis Hubs and Authorities: HITS Page Rank Stability

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On Link-based Similarity Join

VLDB 2011. Presenter: Reynold Cheng Department of Computer Science The University of Hong Kong ckcheng@cs.hku.hk. On Link-based Similarity Join. A joint work with: Liwen Sun, Xiang Li, David Cheung (University of Hong Kong) Jiawei Han (University of Illinois Urbana Champaign).

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Influence of Audio Biofeedback on Structural Properties of Postural Sway

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Simulated annealing for convex optimization

Simulated annealing for convex optimization. Adam ? . Kalai: TTI-Chicago Santosh Vempala: MIT. Bar Ilan University 2004. 100-million dollar endowment (thanks, Toyoda!) 12 tenure -track slots, 18 visitors On University of Chicago campus Optional teaching Advising graduate students.

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Quantum Circuit Placement: Optimizing Qubit-to-qubit Interactions through Mapping Quantum Circuits into a Physical Exper

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Learning Random Walk Models for Inducing Word Dependency Distributions

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Introduction to Monte Carlo Methods

Introduction to Monte Carlo Methods. D.J.C. Mackay. 1. The problems to be solved. The aim of Monte Carlo methods Problem 1: generating samples from { x ( r ) } r =1 R a given target density P ( x ). Problem 2: estimating expectations of functions

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ECE5320 Mechatronics Assignment#01: Literature Survey on Sensors and Actuators Topic: Bio actuator Artificial Muscle

ECE5320 Mechatronics Assignment#01: Literature Survey on Sensors and Actuators Topic: Bio actuator Artificial Muscle. Prepared by: Soujanya Kuravi Dept. of Electrical and Computer Engineering Utah State University E: ; T: (817)-201-7748; F: (435)797-3054 (ECE Dept.). 3/10/2006. Outline .

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Social Networks 101

Social Networks 101. Prof. Jason Hartline and Prof. Nicole Immorlica. Lecture Ten : The web and PageRank . The internet vs the web. The internet : The world wide web : Nodes = machines Nodes = webpages Edges = wires Edges = hyperlinks. The web is a directed graph. Cows:

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Quantum random walks of interacting particles and the graph isomorphism problem

Quantum random walks of interacting particles and the graph isomorphism problem. Susan N. Coppersmith University of Wisconsin, Madison collaborators: Shiue-Yuan Shiau, Robert Joynt, John Gamble S.-Y. Shiau et al., Quantum Information and Computation 5, 492-506 (2005) Funding: NSF, ARO/NSA.

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Respondent-driven Sampling for Characterizing Unstructured Overlays

Respondent-driven Sampling for Characterizing Unstructured Overlays . Graciously Presented By: Shubho Sen AT&T Labs - Research. Motivation. P2P systems are very popular in practice. Millions of simultaneous users. A significant fraction of Internet traffic

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The Monte Carlo Method Part 2 with maple programming

The Monte Carlo Method Part 2 with maple programming. By: Joe Casey. Review . T he Monte Carlo method solves a problem by directly simulating the underlying (physical) process and then calculating the (average) result of the process. 1. Draw a random number

866 views • 18 slides