PPT-A Framework For Tuning Posterior

Entropy Rajhans Samdani Joint work with MingWei Chang Microsoft Research and Dan Roth University of Illinois at UrbanaChampaign Page 1 Workshop on Inferning ICML 2012 Edinburgh. 49 BelAir 103 Levis Qc Canada G6V 6K9 mrueltopcontrolcom Keywords Process Control Optimization Oscilla tion Model PID Control Closed L oop Open Loop Stiction Backlash Non linearity Pseudorandombinary Sequence Generalized Binary Noise ABSTRACT Tradit Presented . by:. . Rastislav Vajdák. . Senior Test Engineer. . . Oxymat. -Slovakia, s.r.o.. Fine tuning and setting PSA. OC input / output parameters. Tools. Start . o. f system to test . . EM/. Posterior Regularization . (. Ganchev. et al, 10). E-step:. M. -step: . argmax. w. . E. q. . log . P . (. x. , . y. ; . w. ). Hard EM/. Constraint driven-learning (Chang et al, 07). E-step. Paolo Romano. Based on ICAC’14 paper. N. . . Diegues. and Paolo Romano. Self-Tuning Intel Transactional Synchronization Extensions. 11. th . USENIX International Conference . on Autonomic Computing (. When and How to Improve Code Performance?. Ivaylo Bratoev. Telerik Corporation. www.telerik.com. Actual . vs. Perceived Performance. Example: . “Vista's file copy performance is noticeably worse than Windows XP” . RED. for Web Traffic. Mikkel Christiansen, Kevin Jeffay,. David Ott, Donelson Smith. UNC, Chapel Hill. SIGCOMM 2000, Stockholm. subsequently. IEEE/ACM Transactions on Networking. Vol. 9 ,  No. 3  (June 2001) pp 249 – 264.. By Hilary . Janysek. What’s the difference?. Intonation: . . (noun) The correct or accurate pitching (placement) of intervals; the capacity to play or sing in tune.. Tuning: . . (verb) to adjust to the correct or given standard of pitch. . Chairs. IETF 79. 1. IGMP/MLD tuning milestone. Are we ready to adopt one of these drafts?. draft-asaeda-multimob-igmp-mld-optimization-04.txt. . draft-wu-multimob-igmp-mld-tuning-03.txt . More specific questions follow. by . Auto-tuning. the Managed Java Back-end . Vimuth. Fernando, . Milinda. Fernando, . Tharindu. . Rusira. , . Sanath. . Jayasena. Department of Computer Science and Engineering . University of . John H. . Yopp. , PhD. Strategic Partnerships, Tuning USA. David W. Marshall, PhD. Associate Director, Tuning USA. Presentation Goals. Discuss how the unique . f. eatures of U.S. higher education versus other national educational . The Tuning Process. Benefits of Tuning. Why Tuning is Different. What is Tuning?. A collaborative, faculty-driven . process that . “harmonizes” curricula around defining what a . student should know and be able to do in a chosen . process that identifies what a student should know and be able to do in a chosen . discipline at the completion of a degree by:. Defining areas of competency. Identifying learning outcomes. Scaling competencies and outcomes to degree level. Tim . Birtwistle. 2. Why ……………………….?. Tuning is a five step process comprised of: . defining the discipline core; . mapping employability; . surveying stakeholders; . honing core competencies and learning outcomes; . regarded in Australia for his tuning mastery, Eric has been evolving the performance of vehicles for over 15 years. He was passionate about cars from a very early age, and had an innate curiosity to