Difference between revisions of "Class meeting for 10-605 LDA"
From Cohen Courses
Jump to navigationJump to search (→Slides) |
|||
| Line 22: | Line 22: | ||
* [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://dl.acm.org/citation.cfm?id=2623756 Reducing the sampling complexity of topic models], Li, Ahmed, Ravi, & Smola, KDD 2014 | ||
| − | * [ | + | * [https://dl.acm.org/citation.cfm?id=2741682 A Scalable Asynchronous Distributed Algorithm for Topic Modeling], Yu, Hsieh, Yun, Vishwanathan, Dillon, WWW 2015 |
=== Things to remember === | === Things to remember === | ||
Revision as of 16:28, 19 November 2017
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2016.
Contents
Slides
- Lecture 1: Powerpoint, PDF.
- Lecture 2: Powerpoint, PDF.
Quiz
- No quiz for lecture 1
- Quiz for lecture 2
Readings
Basic LDA:
- Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent Dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.
Speedups for LDA:
- 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
- A Scalable Asynchronous Distributed Algorithm for Topic Modeling, Yu, Hsieh, Yun, Vishwanathan, Dillon, WWW 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.
