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Regular version of the site

Book chapter

Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochastic Matrix Factorization

P. 29-46.
Konstantin Vorontsov, Anna Potapenko.

Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis. In this tutorial we introduce a novel non-Bayesian approach, called Additive Regularization of Topic Models. ARTM is free of redundant probabilistic assumptions and provides a simple inference for many combined and multi-objective topic models.

In book

Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochastic Matrix Factorization
Vol. 436: Analysis of Images, Social Networks and Texts. Third International Conference, AIST 2014 Yekaterinburg, Russia, April 10–12, 2014 Revised Selected Papers. Cham: Springer, 2014.