24:54
A2.F — Linearly Representable Submodular Functions: An Algebraic Algorithm for Minimization
Saarland Informatics Campus
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Submodularity - Stefanie Jegelka - MLSS 2017
Max Planck Institute for Intelligent Systems
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Recent Progress on Submodular Function Minimization
Simons Institute for the Theory of Computing
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1:04:01
Submodularity: Theory and Applications I
Simons Institute for the Theory of Computing
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EE596B Lecture 4, Submodular Functions, Optimization, and Applications to Machine Learning
Jeffrey A. Bilmes
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1:35:36
EE596B Lecture 7, Submodular Functions, Optimization, & Applications to Machine Learning
Jeffrey A. Bilmes
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1:27:49
Lecture 19, Submodular Functions, Optimization, & Applications to Machine Learning
Jeffrey A. Bilmes
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Lecture 11, Submodular Functions, Optimization, & Applications to Machine Learning
Jeffrey A. Bilmes
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Lecture 18, Submodular Functions, Optimization, & Applications to Machine Learning
Jeffrey A. Bilmes
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29:10
Quotient Sparsification for Submodular Functions
Simons Institute for the Theory of Computing
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Lecture 16, Submodular Functions, Optimization, & Applications to Machine Learning
Jeffrey A. Bilmes
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Lecture 8, Submodular Functions, Optimization, & Applications to Machine Learning
Jeffrey A. Bilmes
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Lecture 15, Submodular Functions, Optimization, & Applications to Machine Learning
Jeffrey A. Bilmes
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53:58
Beyond Submodular Maximization via One-Sided Smoothness and Meta-Submodularity
Google TechTalks
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1:37:01
EE596B Lecture 3, Submodular Functions, Optimization, and Applications to Machine Learning
Jeffrey A. Bilmes
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1:45:49
Lecture 9, Submodular Functions, Optimization, & Applications to Machine Learning
Jeffrey A. Bilmes
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1:42:10
Lecture 17, Submodular Functions, Optimization, & Applications to Machine Learning
Jeffrey A. Bilmes
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