Scaling up Dynamic Topic Models

Abstract

Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods are all batch algorithms that scan the full dataset before each update of the model and make inexact variational approximations with mean-field assumptions. Due to a lack of a more… (More)
DOI: 10.1145/2872427.2883046

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Cite this paper

@inproceedings{Bhadury2016ScalingUD, title={Scaling up Dynamic Topic Models}, author={Arnab Bhadury and Jianfei Chen and Jun Zhu and Shixia Liu}, booktitle={WWW}, year={2016} }