ArticleAdvances in neural information processing systems2018
Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions.
Article in Advances in neural information processing systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions.Advances in neural information processing systems · 2018Article
- Submodular Generalized Matching for Peptide Identification in Tandem Mass Spectrometry.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
3 authors.
Funding
Abstract
We study the problem of maximizing deep submodular functions (DSFs) [13, 3] subject to a matroid constraint. DSFs are an expressive class of submodular functions that include, as strict subfamilies, the facility location, weighted coverage, and sums of concave composed with modular functions. We use a strategy similar to the continuous greedy approach [6], but we show that the multilinear extension of any DSF has a natural and computationally attainable concave relaxation that we can optimize using gradient ascent. Our results show a guarantee of
Identifiers
30705579PMC6351064What OpenQuestion holds
Registered trials
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