Summary of Extended Deep Submodular Functions, by Seyed Mohammad Hosseini et al.
Extended Deep Submodular Functions
by Seyed Mohammad Hosseini, Arash Jamshid, Seyed Mahdi Noormousavi, Mahdi Jafari Siavoshani, Naeimeh Omidvar
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Discrete Mathematics (cs.DM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel category of Extended Deep Submodular functions (EDSFs) is introduced, which are neural network-representable and build upon Deep Submodular Functions (DSFs). EDSFs inherit DSF properties while addressing limitations. They can represent all monotone submodular functions, a notable enhancement over DSFs. This family of EDSFs is equivalent to the family of all monotone set functions. Additionally, they maintain concavity when components are non-negative real numbers, essential for certain combinatorial optimization problems. Experimental results show that EDSFs have significantly lower empirical generalization error than DSFs in learning coverage functions, suggesting improved generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EDSFs are a new type of set function that can be represented using neural networks. They’re an improvement over existing Deep Submodular Functions (DSFs). EDSFs can do everything DSFs can do, but also represent some extra types of functions. This makes them more powerful and useful for certain problems. The researchers tested EDSFs and found they were better than DSFs at learning certain types of functions. |
Keywords
» Artificial intelligence » Generalization » Neural network » Optimization
