Difference between revisions of "Class meeting for 10-605 LDA"
From Cohen Courses
Jump to navigationJump to search| Line 19: | Line 19: | ||
* The time complexity and storage requirements of Gibbs sampling for LDAs. | * The time complexity and storage requirements of Gibbs sampling for LDAs. | ||
* How LDA learning can be sped up using IPM approaches. | * How LDA learning can be sped up using IPM approaches. | ||
| − | |||
Revision as of 18:32, 6 December 2015
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2015.
Slides
Quiz: https://qna-app.appspot.com/view.html?aglzfnFuYS1hcHByGQsSDFF1ZXN0aW9uTGlzdBiAgICg2LfLCww
Readings
- Distributed Algorithms for Topic Models, Newman et al, JMLR 2009.
- Efficient Methods for Topic Model Inference on Streaming Document Collections, Yao, Mimno, McCallum KDD 2009.
Things to remember
- How Gibbs sampling is used to sample from a model.
- The "generative story" associated with key models like LDA, naive Bayes, and stochastic block models.
- What a "mixed membership" generative model is.
- The time complexity and storage requirements of Gibbs sampling for LDAs.
- How LDA learning can be sped up using IPM approaches.
