Online Algorithms for Sum-Product Networks with Continuous Variables
Abstract
Sum-product networks (SPNs) have recently emerged as an attractive representation due to their dual interpretation as a special type of deep neural network with clear semantics and a tractable probabilistic graphical model. We explore online algorithms for parameter learning in SPNs with continuous variables. More specifically, we consider SPNs with Gaussian leaf distributions and show how to derive an online Bayesian moment matching algorithm to learn from streaming data. We compare the resulting generative models to stacked restricted Boltzmann machines and generative moment matching networks on real-world datasets.
Cite
Text
Jaini et al. "Online Algorithms for Sum-Product Networks with Continuous Variables." Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 2016.Markdown
[Jaini et al. "Online Algorithms for Sum-Product Networks with Continuous Variables." Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 2016.](https://mlanthology.org/pgm/2016/jaini2016pgm-online/)BibTeX
@inproceedings{jaini2016pgm-online,
title = {{Online Algorithms for Sum-Product Networks with Continuous Variables}},
author = {Jaini, Priyank and Rashwan, Abdullah and Zhao, Han and Liu, Yue and Banijamali, Ershad and Chen, Zhitang and Poupart, Pascal},
booktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models},
year = {2016},
pages = {228-239},
volume = {52},
url = {https://mlanthology.org/pgm/2016/jaini2016pgm-online/}
}