Distributed MAP Inference for Undirected Graphical Models
published: Jan. 13, 2011, recorded: December 2010, views: 256
Report a problem or upload filesIf 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.
In this work, we distribute the MCMC-based MAP inference using the Map-Reduce framework. The variables are assigned randomly to machines, which leads to some factors that neighbor vari- ables on separate machines. Parallel MCMC-chains are initiated using proposal distributions that only suggest local changes such that factors that lie across machines are not examined. After a fixed number of samples on each machine, we redistribute the variables amongst the machines to enable proposals across variables that were on different machines. To demonstrate the distribution strategy on a real-world information extraction application, we model the task of cross-document coreference.
Download slides: nipsworkshops2010_singh_dmapi_01.pdf (1.7 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !