PPT-Consensus Relevance with Topic and Worker Conditional Model

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Paul N Bennett Microsoft Research Joint with Ece Kamar Microsoft Research Gabriella Kazai Microsoft Research Cambridge Motivation for Consensus Task Recover actual

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Consensus Relevance with Topic and Worker Conditional Model: Transcript

Paul N Bennett Microsoft Research Joint with Ece Kamar Microsoft Research Gabriella Kazai Microsoft Research Cambridge Motivation for Consensus Task Recover actual relevance of a topicdocument . Professor William Greene. Stern School of Business. Department . of Economics. Econometrics I. Part . 2 – Projection and. Regression. Statistical Relationship. Objective. : Characterize the ‘relationship’ between a variable of interest and a set of 'related' variables . What are the advantages and disadvantages . CONSENSUS – decisions are not made unless everyone agrees.. Issues are debated and discussed until everyone agrees on a course of action. Consensus decision-making is relatively rare for government. In most democracies, decisions are made according to a majority vote. Because the representatives belong to different political parties, regions, and so forth, consensus in a democracy is difficult to achieve.. Paul N. Bennett, Microsoft Research. Joint with. Ece Kamar, Microsoft Research. Gabriella Kazai, Microsoft Research Cambridge. Motivation for Consensus Task. Recover actual . relevance of . a topic-document . Ching. -Chun Hsiao. 1. Outline. Problem description. Why conditional random fields(CRF). Introduction to CRF. CRF model. Inference of CRF. Learning of CRF. Applications. References. 2. Reference. 3. Charles . Ken Birman. Fall 2012. Consensus… a classic problem. Consensus abstraction underlies many distributed systems and protocols. N processes. They start execution with inputs.  {0,1}. Asynchronous, reliable network. Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. Discovery. In . Social Networks. Review of Topic Model. Review of Joint/Conditional Distributions. What do the following tell us:. P(. Z. i. ). P(. Z. i. | {W,D}). P(. θ. . | {W,D}). Extending The Topic Model. with Overlapping Data Inference. Esben Hedegaard and Bob Hodrick. Arizona State Univ. Columbia and. . NBER . for Pseudo–Relevance Feedback . Yuanhua . Lv. . & . ChengXiang. . Zhai. Department of Computer Science, UIUC. Presented by Bo Man . 2014/11/18. Positional Relevance Model . for Pseudo–Relevance Feedback . WSLD 2012. What is CBDM?. CBDM stands for Consensus-Based-Decision-Making. Consensus . may be defined . as . an acceptable resolution, one that can be supported, even if not the "favorite" of each individual. . Extracting Search-Focused Key N-Grams for Relevance Ranking in Web Search Date: 2012/11/29 Author: Chen Wang, Keping Bi, Yunhua Hu, Hang Li, Guihong Cao Source: WSDM’12 Advisor: Jia -ling, cooperative 30eld in order to create a fairer economy and better jobs build local wealth and create meaningful change for underserved populations Created by the US Federation of Worker Cooperatives th In . Social Networks. Review of Topic Model. Review of Joint/Conditional Distributions. What do the following tell us:. P(. Z. i. ). P(. Z. i. | {W,D}). P(. Z. i. . , . Z. j. . | {W,D}). Extending The Topic Model. Prepare Maine Social Worker, Licensed-Conditional License Practice Course
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