'Markov logic' diaporamas de présentation

Statistical Relational Learning for Knowledge Extraction from the Web

1.09k views • 91 slides

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

944 views • 84 slides

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

918 views • 84 slides

Unifying Logical and Statistical AI

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

939 views • 82 slides

Markov Logic

Markov Logic. Pedro Domingos Dept. of Computer Science & Eng. University of Washington. Desiderata. A language for cognitive modeling should: Handle uncertainty Noise Incomplete information Ambiguity Handle complexity Many objects Relations among them IsA and IsPart hierarchies Etc.

1k views • 78 slides

Machine Learning For the Web: A Unified View

Machine Learning For the Web: A Unified View. Pedro Domingos Dept. of Computer Science & Eng. University of Washington Includes joint work with Stanley Kok, Daniel Lowd, Hoifung Poon, Matt Richardson, Parag Singla, Marc Sumner, and Jue Wang. Overview. Motivation Background Markov logic

831 views • 73 slides

Markov Logic: A Unifying Language for Information and Knowledge Management

Markov Logic: A Unifying Language for Information and Knowledge Management. Pedro Domingos Dept. of Computer Science & Eng. University of Washington Joint work with Stanley Kok, Daniel Lowd, Hoifung Poon, Matt Richardson, Parag Singla, Marc Sumner, and Jue Wang. Overview. Motivation

937 views • 73 slides

Markov Logic: A Simple and Powerful Unification Of Logic and Probability

Markov Logic: A Simple and Powerful Unification Of Logic and Probability. Pedro Domingos Dept. of Computer Science & Eng. University of Washington Joint work with Stanley Kok, Daniel Lowd, Hoifung Poon, Matt Richardson, Parag Singla, Marc Sumner, and Jue Wang. Overview. Motivation

863 views • 67 slides

Markov Logic: A Representation Language for Natural Language Semantics

Markov Logic: A Representation Language for Natural Language Semantics. Pedro Domingos Dept. Computer Science & Eng. University of Washington (Based on joint work with Stanley Kok, Matt Richardson and Parag Singla). Overview. Motivation Background Representation Inference Learning

681 views • 54 slides

Markov Logic networks

A brief introduction to. Markov Logic networks. Markov Networks. Undirected graphical models. Smoking. Cancer. Asthma. Cough. Potential functions defined over cliques. Markov Networks. Undirected graphical models. Smoking. Cancer. Asthma. Cough. Log-linear model:.

420 views • 39 slides

Markov Logic And other SRL Approaches

Markov Logic And other SRL Approaches. Overview. Statistical relational learning Markov logic Basic inference Basic learning. Statistical Relational Learning. Goals: Combine (subsets of) logic and probability into a single language Develop efficient inference algorithms

721 views • 35 slides

Recursive Random Fields

Recursive Random Fields. Daniel Lowd University of Washington (Joint work with Pedro Domingos). One-Slide Summary. Question: How to represent uncertainty in relational domains? State-of-the-Art: Markov logic [Richardson & Domingos, 2004]

349 views • 28 slides

Coreference Based Event-Argument Relation Extraction on Biomedical Text

Coreference Based Event-Argument Relation Extraction on Biomedical Text. Katsumasa Yoshikawa 1) , Sebastian Riedel 2) , Tsutomu Hirao 3) , Masayuki Asahara 1) , Yuji Matsumoto 1) 1) Nara Institute of Science and Technology, Japan 2) University of Massachusetts, Amherst, USA

413 views • 27 slides

Jointly Identifying Temporal Relations with Markov Logic

401 views • 26 slides

Markov Logic Networks: A Step Towards a Unified Theory of Learning and Cognition

Markov Logic Networks: A Step Towards a Unified Theory of Learning and Cognition. Pedro Domingos Dept. of Computer Science & Eng. University of Washington Joint work with Jesse Davis, Stanley Kok, Daniel Lowd, Hoifung Poon, Matt Richardson, Parag Singla, Marc Sumner. One Algorithm.

398 views • 23 slides

Recursive Random Fields

Recursive Random Fields. Daniel Lowd University of Washington June 29th, 2006 (Joint work with Pedro Domingos). One-Slide Summary. Question: How to represent uncertainty in relational domains? State-of-the-Art: Markov logic [Richardson & Domingos, 2004]

313 views • 19 slides

Modeling Speech Acts and Joint Intentions in Modal Markov Logic

Modeling Speech Acts and Joint Intentions in Modal Markov Logic. Henry Kautz University of Washington. Goal. Unified way to specify and reason about Communicative actions Domain specific actions Joint and individual obligations Beliefs of agents about other agents Criteria

246 views • 15 slides

Eun Y. Ha, Alok Baikadi , Carlyle Licata, Bradford Mott, James Lester SIGDIAL 2010

Exploring the Effectiveness of Lexical Ontologies for Modeling Temporal Relations with Markov Logic. Eun Y. Ha, Alok Baikadi , Carlyle Licata, Bradford Mott, James Lester SIGDIAL 2010. Introduction. Correctly recognize the temporal aspects of events in discourse.

211 views • 13 slides

Markov Logic: A Unifying Framework for Statistical Relational Learning

Markov Logic: A Unifying Framework for Statistical Relational Learning. Pedro Domingos pedrod@cs.washington.edu Matthew Richardson mattr@cs.washington.edu Department of Computer Science and Engineeering , University of Washington, Seattle, WA 98195-2350, USA.

261 views • 7 slides

Modeling Speech Acts and Joint Intentions in Markov Logic

Modeling Speech Acts and Joint Intentions in Markov Logic. Henry Kautz University of Washington. Goal. Infer and track the goals, intentions, and beliefs of the participants in a meeting Sources of information: Communicative actions Gestures Documents (agenda, minutes, emails)

214 views • 6 slides