DTs: Dynamic Trees
Abstract
In this paper we introduce a new class of image models, which we call dynamic trees or DTs. A dynamic tree model specifies a prior over a large number of trees, each one of which is a tree-structured belief net (TSBN). Experiments show that DTs are capable of generating images that are less blocky, and the models have better translation invariance properties than a fixed, "balanced" TSBN. We also show that Simulated Annealing is effective at finding trees which have high posterior probability.
Cite
Text
Williams and Adams. "DTs: Dynamic Trees." Neural Information Processing Systems, 1998.Markdown
[Williams and Adams. "DTs: Dynamic Trees." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/williams1998neurips-dts/)BibTeX
@inproceedings{williams1998neurips-dts,
title = {{DTs: Dynamic Trees}},
author = {Williams, Christopher K. I. and Adams, Nicholas J.},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {634-640},
url = {https://mlanthology.org/neurips/1998/williams1998neurips-dts/}
}