'Graphical models' diaporamas de présentation

Padhraic Smyth Information and Computer Science University of California, Irvine datalab.uci

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Tomasz Malisiewicz tomasz@cmu Advanced Machine Perception February 2006

A Bayesian Hierarchical Model for Learning Natural Scene Categories L. Fei-Fei and P. Perona. CVPR 2005 Discovering objects and their location in images J. Sivic, B. Russell, A. Efros, A. Zisserman and B. Freeman. ICCV 2005. Tomasz Malisiewicz tomasz@cmu.edu Advanced Machine Perception

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Probability and Statistics Review

Probability and Statistics Review. Thursday Sep 11. The Big Picture. Probability. Model. Data. Estimation/learning. But how to specify a model?. Graphical Models. How to specify the model? What are the variables of interest? What are their ranges? How likely their combinations are?

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Graphical Models in Vision .

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3-D Scene Analysis via Sequenced Predictions over Points and Regions

3-D Scene Analysis via Sequenced Predictions over Points and Regions. Daniel Munoz. Drew Bagnell. Martial Hebert. Xuehan Xiong. Problem: 3D Scene Understanding . Solution: Contextual C lassification. B uilding. W ire. P ole. V eg. T runk. C ar. G round.

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A lgorithms and S pecializers for P rovably Optimal I mplementations with R esiliency and E fficiency

A lgorithms and S pecializers for P rovably Optimal I mplementations with R esiliency and E fficiency. Elad Alon , Krste Asanovic (Director) , Jonathan Bachrach , Jim Demmel , Armando Fox, Kurt Keutzer , Borivoje Nikolic , David Patterson, Koushik Sen , John Wawrzynek

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Lecture 17 Gaussian Mixture Models and Expectation Maximization

Lecture 17 Gaussian Mixture Models and Expectation Maximization. Machine Learning. Last Time. Logistic Regression. Today. Gaussian Mixture Models Expectation Maximization. The Problem. You have data that you believe is drawn from n populations

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Gaussian Mixture Models and Expectation Maximization

Gaussian Mixture Models and Expectation Maximization. Machine Learning. Last Time. Review of Supervised Learning Clustering K-means Soft K-means. Today. Gaussian Mixture Models Expectation Maximization. The Problem. You have data that you believe is drawn from n populations

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Expectation Maximization

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Intro to Computational Fluid Dynamics

Intro to Computational Fluid Dynamics. Brandon Lloyd COMP 259 April 16, 2003. Image courtesy of Prof. A. Davidhazy at RIT. Used without permission. ?. Overview. Understanding the Navier-Stokes equations Derivation (following [Griebel 1998]) Intuition Solving the Navier-Stokes equations

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The Problem with the Blank Slate Approach

The Problem with the Blank Slate Approach. Derek Hoiem Beckman Institute, UIUC. Scene Understanding: What We Have. 7. TENT. DOG. GROUND. Scene Understanding: What We Have (More Realistically). 7. CAR. GROUND. Scene Understanding: What We Want.

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Principal Component Analysis

Principal Component Analysis. Machine Learning. Last Time. Expectation Maximization in Graphical Models Baum Welch. Now. Unsupervised Dimensionality Reduction. Curse of Dimensionality. In (nearly) all modeling approaches, more features (dimensions) require (a lot) more data

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Mean Field Inference in Dependency Networks: An Empirical Study

Mean Field Inference in Dependency Networks: An Empirical Study. Daniel Lowd and Arash Shamaei University of Oregon. Learning and Inference in Graphical Models. We want to learn a probability distribution from data and use it to answer queries.

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Accelerating Belief Propagation in Hardware

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Geometry of Rank Tests Fundamentals for Finding Somitogenesis Clock Genes Jason Morton, Lior Pachter, Anne Shiu, Bernd S

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Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning. Chapter 8: graphical models. Bayesian Networks. Directed Acyclic Graph (DAG). Bayesian Networks. General Factorization. Conditional Independence. a is independent of b given c Equivalently Notation. Conditional Independence: Example 1.

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Section 1-1

Section 1-1. Models and Solving Equations. Section 6-1 Topics. numerical models algebraic models graphical models solving factored equations fundamental connection grapher failure and hidden behavior. Numerical Models. data is analyzed to gain insights into phenomena

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Error Correcting Codes

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R. Stranders , A. Farinelli , A. Rogers, N. R. Jennings

Decentralised Coordination of Continuously Valued Control Parameters using the Max-Sum Algorithm. R. Stranders , A. Farinelli , A. Rogers, N. R. Jennings School of Electronics and Computer Science University of Southampton, UK. Motivation. Approach

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Significance: gives for the first time exact inference results in closed-form

Inference with Heavy-Tails in Linear Models. Danny Bickson and Carlos Guestrin. Motivation: Large Scale Network modeling. Inference in the Fourier domain. Application: multiuser detection. Main result 1: exact inference in LCM with stable distributions. Huge amounts of data.

132 views • 1 slides