Markov Logic and Deep Networks

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# markov logic and deep networks - PowerPoint PPT Presentation

Markov Logic and Deep Networks. Pedro Domingos Dept. of Computer Science &amp; Eng. University of Washington. Weight of formula i. No. of true instances of formula i in x. Markov Logic Networks. Basic idea: Use first-order logic to compactly specify large non-i.i.d. models

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### Markov Logic and Deep Networks

Pedro Domingos

Dept. of Computer Science & Eng.

University of Washington

Weight of formula i

No. of true instances of formula i in x

Markov Logic Networks
• Basic idea: Use first-order logic tocompactly specify large non-i.i.d. models
• MLN = Set of formulas with weights
• Formula = Feature template (Vars→Objects)
• E.g., HMM:

State(+s,t) ^ State(+s\',t+1)

Obs(+o,t) ^ State(+s,t)

State of the Art in MLNs
• Many algorithms for learning and inference
• Inference: Millions of variables, billions of features
• Learning: Generative, discriminative, max-margin, etc.
• Best-performing solutions in many application areas
• Natural language, robot mapping, social networks,computational biology, activity recognition, etc.
• Open-source software/Web site: Alchemy
• Book: Domingos & Lowd, Markov Logic,Morgan & Claypool, 2009.

alchemy.cs.washington.edu

Deep Uses of MLNs
• Very large scale inference
• Defining architecture of deep networks
• Adding knowledge to deep networks
• Transition to natural language input
• Learning with many levels of hidden variables
MLNs for Deep Learning
• Basic idea: Use small amounts of knowledgeand large amounts of joint inferenceto make up for lack of supervision
• Relational clustering:
• Cluster objects with similar relations to similar objects
• Cluster relations that hold between similar sets of objects
• Coreference resolution: Outperforms supervised approaches on MUC and ACE benchmarks
• Semantic network extraction: Learns thousands of concepts and relations from millions of Web triples
Example:Unsupervised Semantic Parsing