Skip to content

Toward a general, scaleable framework for Bayesian teaching with applications to topic models

License

Notifications You must be signed in to change notification settings

BaxterEaves/ijcai-iml-2016

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Teaching topic models

Code for our submission to the 2016 IJCAI workshop on Interactive Machine Learning: Connecting Humans and Machines, "Toward a general, scaleable framework for Bayesian teaching with applications to topic models" (ArXiv Preprint).

Installation

Add the install directory to your PYTHONPATH and execute

python setup.py develop

To run tests:

python setup.py test

Tested on OSX, Ubuntu, and REHL.

Use

Run some of the scripts in the ldateach/experiments directory. Some of these may take more time than you are willing to wait, so feel free to tweak the number of runs, samples, etc to bring the runtime down.

License

GNU general public license v3 (GPLv3)

About

Toward a general, scaleable framework for Bayesian teaching with applications to topic models

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published