home › event - improving performance of topic models by variable grouping

EVENT:

Improving performance of topic models by variable grouping
Conferences & Talks

 

description

Topic models have a wide range of applications, including modeling of text documents, images, user preferences, product rankings, and many others. However, learning optimal models may be difficult, especially for large problems. The reason is that inference techniques such as Gibbs sampling often converge to suboptimal models due to the abundance of local minima in large datasets. In this paper, we propose a general method of improving the performance of topic models. The method, called 'grouping transform,' works by introducing auxiliary variables which represent assignments of the original model tokens to groups. Using these auxiliary variables, it becomes possible to re-sample an entire group of tokens at a time. This allows the sampler to make larger state space moves. As a result, better models are learned and performance is improved. The proposed ideas are illustrated on several topic models and several text and image datasets. We show that the grouping transform significantly improves performance over standard models.

 

upcoming events   view all 

Having Natural Conversations with Our Things
Ron Kaplan, Cathy Pearl, Abi Jones, Kyle Dent
18 January 2018 | George E. Pake Auditorium, PARC
PARC Forum  

Beyond Print — Deploying Open Innovation in the 21st Century
Tolga Kurtoglu
23 January 2018 | Las Vegas, NV
Conferences & Talks  

The Cutting Edge of Artificial Intelligence
Tolga Kurtoglu, Margareta Ackerman, Andra Keay, Piero Scaruffi, Allen Saakyan
22 February 2018 | George E. Pake Auditorium, PARC
PARC Forum