PPT-CS b553: Algorithms for Optimization and

Learning Parameter Learning with Hidden Variables amp Expectation Maximization Agenda Learning probability distributions from data in the setting of known structure missing data Expectationmaximization EM algorithm. Regrets and . Kidneys. Intro to Online Stochastic Optimization. Data revealed over time. Distribution . of future events is known. Under time constraints. Limits amount of . sampling/simulation. Solve these problems with two black boxes:. Gradient descent. Key Concepts. Gradient descent. Line search. Convergence rates depend on scaling. Variants: discrete analogues, coordinate descent. Random restarts. Gradient direction . is orthogonal to the level sets (contours) of f,. : Algorithms . for Optimization and Learning. Global optimization. 1. Agenda: . Global Optimization. Local search, optimization. Branch and bound search. Online . search. 3. Global . Optimization. min . for Geometry Processing. Justin Solomon. Princeton University. David . Bommes. RWTH Aachen University. This Morning’s Focus. Optimization.. Synonym(-. ish. ):. . Variational. methods.. This Morning’s Focus. Optimization Algorithms. Welcome!. CS4234 . Overview. Optimization Algorithms. http://. www.comp.nus.edu.sg/. ~gilbert/CS4234. Instructor: . Seth Gilbert. Office: . COM2. -323. Office hours: . by appointment. Collin . Bezrouk. 2-24-2015. Discussion Reference. Some of this material comes from . Spacecraft Trajectory Optimization. (Ch. 7) by Bruce Conway.. Optimization Problem Setup. Optimization problems require the following:. Learning. Structure . Learning. Agenda. Learning probability distributions from . example data. To what extent can Bayes net structure be learned?. Constraint methods (inferring conditional independence). Univariate. optimization. x. f. (x). Key Ideas. Critical points. Direct methods. Exhaustive search. Golden section search. Root finding algorithms. Bisection. [More next time]. Local vs. global optimization. Classification of algorithms. The DIRECT algorithm. Divided rectangles. Exploration and Exploitation as bi-objective optimization. Application to High Speed Civil Transport. Global optimization issues. and Applications. David Crandall, Geoffrey Fox. Indiana University Bloomington. SPIDAL Video Presentation. April 7 2017 . Both Pathology/Remote sensing working on 2D moving to 3D images. Each pathology image could have 10 billion pixels, and we may extract a million spatial objects per image and 100 million features (dozens to 100 features per object) per image. We often tile the image into 4K x 4K tiles for processing. We develop buffering-based tiling to handle boundary-crossing objects. For each typical study, we may have hundreds to thousands of pathology images. Bayesian . Networks. agenda. B. ayesian networks. Chain rule for . Bayes . nets. Naïve Bayes models. Independence declarations. D-separation. Probabilistic inference queries. Purposes of . bayesian. Additive manufacturing (AM) is expanding the range of designable geometries, but to exploit this evolving design space new methods are required to find optimum solutions. Finite element based topology The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Michael Kantor. CEO and Founder . Promotion Optimization Institute (POI). First Name. Last Name. Company. Title. Denny. Belcastro. Kimberly-Clark. VP Industry Affairs. Pam. Brown. Del Monte. Director, IT Governance & PMO.