Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process
Abstract
We propose a novel dependent hierarchical Pitman-Yor process model for discrete data. An incremental Monte Carlo inference procedure for this model is developed. We show that inference in this model can be performed in constant space and linear time. The model is demonstrated in a discrete sequence prediction task where it is shown to achieve state of the art sequence prediction performance while using significantly less memory.
Cite
Text
Bartlett et al. "Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process." International Conference on Machine Learning, 2010.Markdown
[Bartlett et al. "Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/bartlett2010icml-forgetting/)BibTeX
@inproceedings{bartlett2010icml-forgetting,
title = {{Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process}},
author = {Bartlett, Nicholas and Pfau, David and Wood, Frank D.},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {63-70},
url = {https://mlanthology.org/icml/2010/bartlett2010icml-forgetting/}
}