Neural conditional random fields

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.


Presented at:
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Chia Laguna, Sardinia, Italy
Year:
2010
Publisher:
JMLR: W&CP
Laboratories:




 Record created 2010-08-26, last modified 2018-03-17

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