Predict and constrain: Modeling cardinality in deep structured prediction

Nataly Brukhim, Amir Globerson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 approaches, but it is a challenge to introduce them into a deep learning framework. 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.

Original languageEnglish
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsJennifer Dy, Andreas Krause
PublisherInternational Machine Learning Society (IMLS)
Pages1046-1054
Number of pages9
ISBN (Electronic)9781510867963
StatePublished - 2018
Event35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018

Publication series

Name35th International Conference on Machine Learning, ICML 2018
Volume2

Conference

Conference35th International Conference on Machine Learning, ICML 2018
Country/TerritorySweden
CityStockholm
Period10/07/1815/07/18

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