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Unifying Logical and Statistical AI

Unifying Logical and Statistical AI

Unifying Logical and Statistical AI. Pedro Domingos Dept. of Computer Science & Eng. University of Washington Joint work with Jesse Davis, Stanley Kok, Daniel Lowd, Aniruddh Nath, Hoifung Poon, Matt Richardson, Parag Singla, Marc Sumner, and Jue Wang. Overview. Motivation Background

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990 views • 84 slides
Markov Logic

Markov Logic

Markov Logic. Stanley Kok Dept. of Computer Science & Eng. University of Washington Joint work with Pedro Domingos, Daniel Lowd, Hoifung Poon, Matt Richardson, Parag Singla and Jue Wang. Overview. Motivation Background Markov logic Inference Learning Software Applications.

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1.1k views • 64 slides
Markov Logic

Markov Logic

Markov Logic. Stanley Kok Dept. of Computer Science & Eng. University of Washington Joint work with Pedro Domingos, Daniel Lowd, Hoifung Poon, Matt Richardson, Parag Singla and Jue Wang. Overview. Motivation Background Markov logic Inference Learning Software Applications.

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851 views • 64 slides
Markov Logic Networks

Markov Logic Networks

Markov Logic Networks. Pedro Domingos Dept. Computer Science & Eng. University of Washington (Joint work with Matt Richardson). Overview. Representation Inference Learning Applications. Markov Logic Networks. A logical KB is a set of hard constraints on the set of possible worlds

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621 views • 44 slides
Online Max-Margin Weight Learning for Markov Logic Networks

Online Max-Margin Weight Learning for Markov Logic Networks

Machine Learning Group Department of Computer Science The University of Texas at Austin. Online Max-Margin Weight Learning for Markov Logic Networks. SDM 2011, April 29, 2011. Tuyen N. Huynh and Raymond J. Mooney. Motivation. Citation segmentation.

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526 views • 36 slides
Statistical Relational Learning

Statistical Relational Learning

Statistical Relational Learning. Pedro Domingos Dept. Computer Science & Eng. University of Washington. Overview. Motivation Some approaches Markov logic Application: Information extraction Challenges and open problems. Motivation. Most learners only apply to i.i.d. vectors

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628 views • 29 slides
Progress Report

Progress Report

Progress Report. ekker. Problem Definition. In cases such as object recognition , we can not include all possible objects for training . So transfer learning could deal with this kind of problem.

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291 views • 14 slides

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