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. | ||
| + | * [http://dl.acm.org/citation.cfm?id=2623756 Reducing the sampling complexity of topic models], Li, Ahmed, Ravi, & Smola, KDD 2014 | ||
| + | * [http://arxiv.org/abs/1412.1576 LightLDA: Big Topic Models on Modest Compute Clusters], Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric P. Xing, Tie-Yan Liu, Wei-Ying Ma, 2015 | ||
=== Things to remember === | === Things to remember === | ||
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* 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. | ||
| + | |||
| + | * Why efficient sampling is important for LDAs | ||
| + | * How sampling can be sped up for many topics by preprocessing the parameters of the distribution | ||
| + | * How the storage used for LDA can be reduced by exploiting the fact that many words are rare. | ||
Revision as of 15:58, 11 August 2016
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2016.
Slides
- TBD
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.
- Reducing the sampling complexity of topic models, Li, Ahmed, Ravi, & Smola, KDD 2014
- LightLDA: Big Topic Models on Modest Compute Clusters, Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric P. Xing, Tie-Yan Liu, Wei-Ying Ma, 2015
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.
- Why efficient sampling is important for LDAs
- How sampling can be sped up for many topics by preprocessing the parameters of the distribution
- How the storage used for LDA can be reduced by exploiting the fact that many words are rare.
