# Scalable inference of topic evolution via models for latent geometric structures

arXiv: https://arxiv.org/abs/1809.08738 · HTML: https://mlsanity.com/paper/1809.08738/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Bayesian Federated Neural Matching that Completes Full Information](https://mlsanity.com/paper/2211.08010/) (2022)
- [Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networks](https://mlsanity.com/paper/2210.12974/) (2022)

## Abstract (arXiv)

We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference. Our model is nonparametric Bayesian and the corresponding inference algorithm is able to discover new topics as the time progresses. By exploiting the connection between the modeling of topic polytope evolution, Beta-Bernoulli process and the Hungarian matching algorithm, our method is shown to be several orders of magnitude faster than existing topic modeling approaches, as demonstrated by experiments working with several million documents in under two dozens of minutes.