Neural Conditional Random Fields
Abstract
We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Markov networks, yielding a powerful and scalable probabilistic model that we apply to signal labeling tasks.
Cite
Text
Do and Artieres. "Neural Conditional Random Fields." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Do and Artieres. "Neural Conditional Random Fields." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/do2010aistats-neural/)BibTeX
@inproceedings{do2010aistats-neural,
title = {{Neural Conditional Random Fields}},
author = {Do, Trinh–Minh–Tri and Artieres, Thierry},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {177-184},
volume = {9},
url = {https://mlanthology.org/aistats/2010/do2010aistats-neural/}
}