PPT-Markov Logic Networks: Exploring their Application to Socia

Author : lindy-dunigan | Published Date : 2015-11-29

Parag Singla Dept of Computer Science and Engineering Indian Institute of Technology Delhi Joint work with people at University of Washington and IIT Delhi Overview

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Markov Logic Networks: Exploring their Application to Socia: Transcript

Parag Singla Dept of Computer Science and Engineering Indian Institute of Technology Delhi Joint work with people at University of Washington and IIT Delhi Overview Motivation Markov logic. Sai. Zhang. , . Congle. Zhang. University of Washington. Presented. . by . Todd Schiller. Software bug localization: finding the likely buggy code fragments. A . software. system. (. source code. Alan Ritter. Markov Networks. Undirected. graphical models. Cancer. Cough. Asthma. Smoking. Potential functions defined over cliques. Smoking. Cancer. . Ф. (S,C). False. False. 4.5. False. True. Alan Ritter. Problem: Non-IID Data. Most real-world data is not IID. (like coin flips). Multiple correlated variables. Examples:. Pixels in an image. Words in a document. Genes in a microarray. We saw one example of how to deal with this. Hao. Wu. Mariyam. Khalid. Motivation. Motivation. How would we model this scenario?. Motivation. How would we model this scenario?. Logical Approach. Motivation. How would we model this scenario?. Logical Approach. Daniel Lowd. University of Oregon. April 20, 2015. Caveats. The purpose of this talk is to inspire meaningful discussion.. I may be completely wrong.. My background:. Markov logic networks, probabilistic graphical models. (Markov Nets). (Slides from Sam . Roweis. ). Connection to MCMC:. . . MCMC requires sampling a node given its . markov. blanket. . Need to use P(. x|MB. (x)). . . For . Bayes. nets MB(x) contains more. Logic and Probability. Parag Singla. Dept. of Computer Science & Engineering. Indian Institute of Technology Delhi. Overview. Motivation & Background. Markov logic. Inference & Learning. Abductive. Model Definition. Comparison to Bayes Nets. Inference techniques. Learning Techniques. A. B. C. D. Qn. : What is the. . most likely. . configuration of A&B?. Factor says a=b=0. But, marginal says. Fehringer. Seminar: Probabilistic Models for Information Extraction. by Dr. Martin . Theobald. and Maximilian . Dylla. . Based on Richards, M., and . Domingos. , P. (2006). Markov Logic Networks. 1. Tushar. . Khot. Joint work with . Sriraam. . Natarajan. , . Kristian. . Kersting. and . Jude . Shavlik. Sneak Peek. Present a method to learn structure and parameter for MLNs . simultaneously. Use functional gradients to learn many . Networks and Communication Department. 1. Outline. Networks and Communication Department. Define and give a brief history of artificial intelligence.. Describe how knowledge is represented in an intelligent agent. Parag. . Singla. & Raymond J. Mooney. Dept. of Computer Science. University of Texas, Austin. Motivation . [ Blaylock & Allen 2005] . Road Blocked!. Road Blocked!. Heavy Snow; Hazardous Driving. Relational. . Learning. . for. . NLP. William. . Y.. . Wang. William W. Cohen. Machine Learning Dept . and Language Technologies. . Inst.. joint work with:. Kathryn Rivard Mazaitis. Outline. Motivation. in Markov Logic using an RDBMS. Feng . Niu. , Chris . Ré. , . AnHai. Doan, and Jude . Shavlik. University of Wisconsin-Madison. One Slide Summary. 2. Machine Reading . is a DARPA program to capture knowledge expressed in free-form text.

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