PPT-Expectation Maximization Meets Sampling in Motif Finding

Zhizhuo Zhang Outline Review of Mixture Model and EM algorithm Importance Sampling Resampling EM Extending EM Integrate Other Features Result Review Motif Finding Mixture modeling Given a dataset . What’s a motif paper?. A motif paper allows you to focus on an aspect of a short story, play, or novel. . By exploring a single motif, you are able to draw conclusions and offer insight into the motivation or message of a particular piece of literature.. Machine Learning. Last Time. Expectation Maximization. Gaussian Mixture Models. Today. EM Proof. Jensen’s Inequality. Clustering sequential data. EM over . HMMs. EM in any Graphical Model. Gibbs Sampling. Honglei. . Zhuang. , . Yihan. Sun, Jie Tang, Jialin Zhang, Xiaoming Sun. Influence Maximization. 0.6. 0.5. 0.1. 0.4. 0.6. 0.1. 0.8. 0.1. A. B. C. D. E. F. Probability . of . influence. Marketer Alice. Three Important Devices: Know These. Symbol: An object that represents something more significant that just itself. Motif: A . recurring. element or idea; a phrase; image; repetition of similar symbols; repetition of an issue/attitude. a distinctive feature or dominant idea in an artistic or literary composition. This is not a hidden message, this is an idea that the author tries to communicate CLEARLY. VIDEO ??? . http://study.com/academy/lesson/motif-in-literature-definition-examples-quiz.html. Xinran He . and David Kempe. University of Southern . California. {xinranhe, . dkempe. }@usc.edu. 08/15/2016. The adoption of new products . can . propagate between nodes . in the social network. 0.8. Pursue . Excellence . – Be the best. Expectation Cards. At Castle View we expect our students to conduct themselves in an exemplary manner at all times and to follow the school rules. . To help students with this, we have introduced an expectation card that all . Rajhans Samdani. Joint work with. Ming-Wei Chang (. Microsoft Research. ) . and Dan Roth. University of Illinois at Urbana-Champaign. Page . 1. NAACL 2012,. Montreal. Weakly Supervised Learning in NLP. Cody Dunne and Ben Shneiderman. {cdunne, ben}@. cs.umd.edu. 30. th. . Annual . Human-Computer Interaction Lab Symposium, May 22–23, 2013. College Park, . MD. Who Uses Network Analysis. Network Analysis is Hard. A link between Continuous-time/Discrete-time Systems. x. (. t. ). y. (. t. ). h. (. t. ). x. [. n. ]. y. [. n. ]. h. [. n. ]. Sampling. x. [. n. ]=. x. (. nT. ), . T. : sampling period. x. [. n. ]. x. [X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= E[X]= Linearity of Expectation: E[X + Y] = E[X] + E[Y]Example: Birthday Paradoxm balls WestCEDC wwwbucuwestcomWestCEDC wwwbucuwestcomBuCu WestWhere Business Meets Culture Artful SIDE OF BODRUMCurrently sought after for its vibrant nightlife and ravishing beaches Bodrum formerly called Halicarnassus has always been remarkable In fact this small coastal town used to harb . The costs that an organization incurs even when there is little or no activity are . fixed costs. , or . overhead. .. Finding Marginal Cost. . Variable costs . are usually associated with labor and raw materials and change with the business’s rate of operation or output..