'Iterative algorithm' diaporamas de présentation

Statistical Methods for Image Reconstruction

Statistical Methods for Image Reconstruction. MIC 2007 Short Course October 30, 2007 Honolulu. Topics. Lecture I: Warming Up (Larry Zeng) Lecture II: ML and MAP Reconstruction (Johan Nuyts) Lecture III: X-Ray CT Iterative Reconstruction (Bruno De Man)

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Algorithms

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Information Bottleneck versus Maximum Likelihood

Information Bottleneck versus Maximum Likelihood. Felix Polyakov. Overview of the talk. Brief review of the Information Bottleneck Maximum Likelihood Information Bottleneck and Maximum Likelihood Example from Image Segmentation. A Simple Example. Simple Example.

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Massively LDPC Decoding on Multicore Architectures

Massively LDPC Decoding on Multicore Architectures. Present by : fakewen. Authors. Gabriel Falcao Leonel Sousa Vitor Silva. Outline. Introduction BELIEF PROPAGATION DATA STRUCTURES AND PARALLEL COMPUTING MODELS PARALLELIZING THE KERNELS EXECUTION EXPERIMENTAL RESULTS. Outline.

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Privacy Preserving OLAP

Privacy Preserving OLAP. Rakesh Agrawal, IBM Almaden Ramakrishnan Srikant, IBM Almaden Dilys Thomas, Stanford University. Horizontally Partitioned Personal Information. Client C 1 Original Row r 1 Perturbed p 1. Table T for analysis at server. Client C 2 Original Row r 2

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Bayesian Networks for Modeling Gene Expression Data

Bayesian Networks for Modeling Gene Expression Data. Sushmita Roy BMI/CS 576 www.biostat.wisc.edu/bmi576 sroy@biostat.wisc.edu Nov 19 th , 2013. Bayesian networks (BN). A BN compactly represents a joint probability distribution It has two parts: A graph which is directed and acyclic

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Authoritative Sources in a Hyperlinked Environment

Authoritative Sources in a Hyperlinked Environment. Jon M. Kleinberg Presentation by Julian Zinn. Searching the Web. Goal: find pages relevant to a query. The basic text-based search algorithms retrieve pages that contain the query keywords.

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QoS Aware Adaptive Subcarrier Allocation in OFDMA Systems

QoS Aware Adaptive Subcarrier Allocation in OFDMA Systems. Mustafa Ergen & Sinem Coleri {ergen,csinem}@eecs.berkeley.edu University of California Berkeley. Introduction. Motivation Orthogonal Frequency Division Multiple Access(OFDMA) OFDMA System Resource Allocation Problem Algorithms

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GRAS SAF System, Products, and Archive

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An Iterative Algorithm for Finding the Minimum Sampling Frequency For Two Bandpass Signals

An Iterative Algorithm for Finding the Minimum Sampling Frequency For Two Bandpass Signals. Yuan-Pei Lin, Yi-De Liu, See-May Phoong. Outline. Problem Formulation Previous Works A New Set of Conditions for Alias-Free Sampling The Proposed Iterative Algorithm Complexity

370 views • 24 slides

Two approximate approaches for solving the large-scale shell model problem

Two approximate approaches for solving the large-scale shell model problem. Sevdalina S. Dimitrova Institute for Nuclear Research & Nuclear Energy Bulgarian Academy of Sciences. Collaborators. ISA Nicola Lo Iudice University of Naples, Italy Antonio Porrino University of Naples, Italy

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???? (Toshihiro Kosaki)

???????????????????? ??????????? ??????????????? Computation of Indexwise Step Sizes Considering Reduction of Complementarity Terms in a Primal-Dual Interior-Point Method for Linear Problems. ???? (Toshihiro Kosaki). Spectral Method( in Optimization) ??????. Introduction General Framework

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Expectation Maximization

Expectation Maximization. Dekang Lin Department of Computing Science University of Alberta. Objectives. Expectation Maximization (EM) is perhaps most often used and mostly half understood algorithm for unsupervised learning. It is very intuitive.

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Summary of lessons on MC techniques

Summary of lessons on MC techniques. Matrix Element Monte Carlo. amplitude calculation. phase space integration. approximations. Parton shower. Hadronization (or Fragmentation). Sara Bolognesi. 31 May 2006. 1. Parton level simulation. Increasing in collider energy.

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Algorithms

The Project for the Establishing the Korea ? Vietnam College of Technology in Bac Giang. Algorithms. April-May 2013. Dr. Youn-Hee Han. Professor/Lecturers. Name : Youn-Hee Han Department: Computer Science and Engineering Contact: 010-3912-0900

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AMDA-TOPCAT use case

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Maximum Likelihood and the Information Bottleneck

Maximum Likelihood and the Information Bottleneck. By Noam Slonim & Yair Weiss 2/19/2003. Overview. Main contribution Defines mapping: ML of mixture models to iterative IB Under some initial conditions, an algorithm for one gives a solution for the other. Theoretical and practical concern

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Recursion

Recursion. Review. Recursive solutions, by definition, are built off solutions to sub-problems . Many times, this will mean simply to compute f(n) by adding something, removing something, or otherwise changing the solution for f(n-1 ).

212 views • 11 slides