Difference between revisions of "Class meeting for 10-605 LDA"
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* [http://jmlr.csail.mit.edu/papers/volume10/newman09a/newman09a.pdf Distributed Algorithms for Topic Models], Newman et al, JMLR 2009. | * [http://jmlr.csail.mit.edu/papers/volume10/newman09a/newman09a.pdf Distributed Algorithms for Topic Models], Newman et al, JMLR 2009. | ||
* [http://people.cs.umass.edu/~mimno/papers/fast-topic-model.pdf Efficient Methods for Topic Model Inference on Streaming Document Collections], Yao, Mimno, McCallum KDD 2009. | * [http://people.cs.umass.edu/~mimno/papers/fast-topic-model.pdf Efficient Methods for Topic Model Inference on Streaming Document Collections], Yao, Mimno, McCallum KDD 2009. | ||
| + | |||
| + | === Things to remember === | ||
| + | |||
| + | * How Gibbs sampling is used for to sample from a model. | ||
| + | * Definition of "mixed membership" generative models. | ||
| + | * To complexity and storage requirements of Gibbs sampling for LDAs. | ||
Revision as of 17:53, 4 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 for to sample from a model.
- Definition of "mixed membership" generative models.
- To complexity and storage requirements of Gibbs sampling for LDAs.
