Results 41 to 50 of about 517 (178)
Improved algorithms for submodular function minimization and submodular flow [PDF]
Proceedings of the thirty-second annual ACM symposium on Theory of computing, 2000 Very recently, two groups of researchers independently developed the first combinatorial, strongly polynomial-time algorithms for submodular function minimization (Iwata, Fleischer, Fujishige; and Schrijver). In this paper, we improve on these algorithms and show that the ideas generated in the design of these algorithms are helpful in other contexts ...
Lisa Fleischer, Satoru Iwata 0001
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International Journal of Distributed Sensor Networks, 2021 In this article, we consider the problem of optimally selecting a subset of transmitters from a transmitter set available to a multiple-input and multiple-output radar network.
Chenggang Wang +3 more
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Continuous submodular function maximization
CoRR, 2020 Continuous submodular functions are a category of generally non-convex/non-concave functions with a wide spectrum of applications. The celebrated property of this class of functions - continuous submodularity - enables both exact minimization and approximate maximization in poly. time.
Bian, Yatao; id_orcid0000-0002-2368-4084 +2 more
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CoRR, 2014 Submodular functions are well-studied in combinatorial optimization, game theory and economics. The natural diminishing returns property makes them suitable for many applications. We study an extension of monotone submodular functions, which we call {\em weakly submodular functions}. Our extension includes some (mildly) supermodular functions.
Allan Borodin, Dai Le, Yuli Ye
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Metric learning with submodular functions [PDF]
Neurocomputing, 2020 Abstract Most of the metric learning mainly focuses on using single feature weights with Lp norms, or the pair of features with Mahalanobis distances to learn the similarities between the samples, while ignoring the potential value of higher-order interactions in the feature space.
Jiajun Pan, Hoel Le Capitaine
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A Submodular Optimization Framework for Imbalanced Text Classification With Data Augmentation
IEEE Access, 2023 In the domain of text classification, imbalanced datasets are a common occurrence. The skewed distribution of the labels of these datasets poses a great challenge to the performance of text classifiers.
Eyor Alemayehu, Yi Fang
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Symmetric Submodular Functions, Uncrossable Functions, and Structural Submodularity
CoRRDiestel, et al. (see Order 35 (2017), JCT-A 167 (2019), arXiv:1805.01439) introduced the notion of abstract separation systems that satisfy a submodularity property, and they call this structural submodularity. Williamson, Goemans, Mihail, and Vazirani (Combinatorica 15 (1995)) call a family of sets $\mathcal{F}$ uncrossable if the following holds: for
Miles Simmons +2 more
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Parallel Submodular Function Minimization
Advances in Neural Information Processing Systems 36, 2023 We consider the parallel complexity of submodular function minimization (SFM). We provide a pair of methods which obtain two new query versus depth trade-offs a submodular function defined on subsets of $n$ elements that has integer values between $-M$ and $M$.
Deeparnab Chakrabarty +3 more
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Near Optimal Dynamic Mobile Advertisement Offloading With Time Constraints
IEEE Access, 2019 Owing to the accuracy and flexibility, mobile advertising has become a very attractive marketing method based on smart mobile terminals. The more common mobile advertisement distribution methods are based on location and content.
Wanru Xu, Chaocan Xiang, Chang Tian
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NeuSub: A Neural Submodular Approach for Citation Recommendation
IEEE Access, 2021 Citation recommendation is a task that aims to automatically select suitable references for a working manuscript. This task has become increasingly urgent as the typical pools of candidates continue to grow, in the order of tens or hundreds of thousands ...
Binh Thanh Kieu +4 more
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