Reified Context Models

author: Jacob Steinhardt, Computer Science Department, Stanford University
published: Dec. 5, 2015,   recorded: October 2015,   views: 1479
Categories

See Also:

Download slides icon Download slides: icml2015_steinhardt_context_models_01.pdf (1.4┬áMB)


Help icon Streaming Video Help

Related Open Educational Resources

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

A classic tension exists between exact inference in a simple model and approximate inference in a complex model. The latter offers expressivity and thus accuracy, but the former provides coverage of the space, an important property for confidence estimation and learning with indirect supervision. In this work, we introduce a new approach, reified context models, to reconcile this tension. Specifically, we let the choice of factors in a graphical model (the contexts) be random variables inside the model itself. In this sense, the contexts are reified and can be chosen in a data-dependent way. Empirically, we show that our approach obtains expressivity and coverage on three sequence modeling tasks.

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: