# Concentrated Document Topic Model

arXiv: https://arxiv.org/abs/2102.04449 · HTML: https://mlsanity.com/paper/2102.04449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 6 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [Distilled Wasserstein Learning for Word Embedding and Topic Modeling](https://mlsanity.com/paper/1809.04705/) (2018)
- [Learning Online Alignments with Continuous Rewards Policy Gradient](https://mlsanity.com/paper/1608.01281/) (2016)
- [Jointly Learning Word Embeddings and Latent Topics](https://mlsanity.com/paper/1706.07276/) (2017)

## Abstract (arXiv)

We propose a Concentrated Document Topic Model(CDTM) for unsupervised text classification, which is able to produce a concentrated and sparse document topic distribution. In particular, an exponential entropy penalty is imposed on the document topic distribution. Documents that have diverse topic distributions are penalized more, while those having concentrated topics are penalized less. We apply the model to the benchmark NIPS dataset and observe more coherent topics and more concentrated and sparse document-topic distributions than Latent Dirichlet Allocation(LDA).