Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Abstract
Many machine learning problems require the prediction of multi-dimensional labels. Such structured prediction models can benefit from modeling dependencies between labels. Recently, several deep learning approaches to structured prediction have been proposed. Here we focus on capturing cardinality constraints in such models. Namely, constraining the number of non-zero labels that the model outputs. Such constraints have proven very useful in previous structured prediction methods, but it is a challenge to introduce them into a deep learning approach. Here we show how to do this via a novel deep architecture. Our approach outperforms strong baselines, achieving state-of-the-art results on multi-label classification benchmarks.
Cite
Text
Brukhim and Globerson. "Predict and Constrain: Modeling Cardinality in Deep Structured Prediction." International Conference on Machine Learning, 2018.Markdown
[Brukhim and Globerson. "Predict and Constrain: Modeling Cardinality in Deep Structured Prediction." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/brukhim2018icml-predict/)BibTeX
@inproceedings{brukhim2018icml-predict,
title = {{Predict and Constrain: Modeling Cardinality in Deep Structured Prediction}},
author = {Brukhim, Nataly and Globerson, Amir},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {659-667},
volume = {80},
url = {https://mlanthology.org/icml/2018/brukhim2018icml-predict/}
}