Large-Scale Learning and Inference: What We Have Learned with Markov Logic Networks

author: Pedro Domingos, Dept. of Computer Science & Engineering, University of Washington
published: Jan. 19, 2010,   recorded: December 2009,   views: 715
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Markov logic allows very large and rich graphical models to be compactly specified. Current learning and inference algorithms for Markov logic can routinely handle models with millions of variables, billions of features, thousands of latent variables, and strong dependencies. In this talk I will give an overview of the main ideas in these algorithms, including weighted satisfiability, MCMC with deterministic dependencies, lazy inference, lifted inference, relational cutting planes, scaled conjugate gradient, relational clustering and relational pathfinding. I will also discuss the lessons learned in developing successive generations of these algorithms and promising ideas for the next round of scaling up.

See Also:

Download slides icon Download slides: nipsworkshops09_domingos_lsliwwlmln_01.pdf (138.8 KB)

Download slides icon Download slides: nipsworkshops09_domingos_lsliwwlmln_01.ppt (136.0 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: