Difference between revisions of "Class meeting for 10-605 LDA"
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* How Gibbs sampling is used to sample from a model. | * 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. | * What a "mixed membership" generative model is. | ||
* 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. | ||
| + | * How sampling can be sped up by ordering topics so that the most probable topics according for Pr(z|...) are tested first | ||
Revision as of 17:31, 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.
- How sampling can be sped up by ordering topics so that the most probable topics according for Pr(z|...) are tested first
