Jun Zhu
Assoc. Prof. @ THU
Adj. Faculty @ CMU
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Gibbs MedLDA

Gibbs MedLDA is a Max-margin Topic Model framework developed under Yahoo_LDA framework. It supports large-scale multi-class and multi-label classification by utilizing multi-core and multi-machine parallelism. This project is released under the Apache License, Version 2.0.

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  • Released the parallel Gibbs sampling code for Gibbs MedLDA topic model on March 10, 2013;
  • Released the non-parallel Gibbs sampling code for Gibbs MedLDA topic model on March 10, 2013;

References

  1. Jun Zhu, Ning Chen, Hugh Perkins, and Bo Zhang. Gibbs Max-margin Topic Models with Fast Sampling Algorithms, In ICML, Atlanta, USA, 2013.

Last updated on Dec. 4th, 2012.