Encoding and Decoding Representations with Sum- and Max-Product Networks
Abstract
Sum-Product Networks (SPNs) are deep density estimators allowing exact and tractable inference. While up to now SPNs have been employed as black-box inference machines, we exploit them as feature extractors for unsupervised Representation Learning. Representations learned by SPNs are rich probabilistic and hierarchical part-based features. SPNs converted into Max-Product Networks (MPNs) provide a way to decode these representations back to the original input space. In extensive experiments, SPN and MPN encoding and decoding schemes prove highly competitive for Multi-Label Classification tasks.
Cite
Text
Vergari et al. "Encoding and Decoding Representations with Sum- and Max-Product Networks." International Conference on Learning Representations, 2017.Markdown
[Vergari et al. "Encoding and Decoding Representations with Sum- and Max-Product Networks." International Conference on Learning Representations, 2017.](https://mlanthology.org/iclr/2017/vergari2017iclr-encoding/)BibTeX
@inproceedings{vergari2017iclr-encoding,
title = {{Encoding and Decoding Representations with Sum- and Max-Product Networks}},
author = {Vergari, Antonio and Peharz, Robert and Di Mauro, Nicola and Esposito, Floriana},
booktitle = {International Conference on Learning Representations},
year = {2017},
url = {https://mlanthology.org/iclr/2017/vergari2017iclr-encoding/}
}