Towards minimizing k-submodular functions
WebTowards Minimizing k-Submodular Functions. Huber, Anna. ; Kolmogorov, Vladimir. In this paper we investigate k-submodular functions. This natural family of discrete functions includes submodular and bisubmodular functions as the special cases k = 1 and k = 2 respectively. In particular we generalize the known Min-Max-Theorem for submodular and ... WebFeb 11, 2024 · In the problem of maximizing non-monotone k-submodular function f under individual size constraints, the goal is to maximize the value of k disjoint subsets with size …
Towards minimizing k-submodular functions
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Websubmodular functions, satisfying f(S) = f(S¯) for all sets S. Symmetric submodular functions have been considered widely in the litrature [13, 32]. It appears that symmetry allows better/simpler approximation results, and thus deserves separate attention. The problem of maximizing a submodular function is of central importance, with special cases WebAug 3, 2016 · Towards minimizing k-submodular functions. In Proceedings of the 2nd International Symposium on Combinatorial Optimization (ISCO’12) ... Alexander Schrijver. …
Web330: Sensitivity Analysis of Deep Neural Networks 332: Migration as Submodular Optimization 333: Scalable Distributed DL Training: Batching Communication and Computation 335: Non-‐Compensatory Psychological Models for Recommender Systems 353: Deep Interest Evolution Network for Click-‐Through Rate Prediction 362: MFBO-‐SSM: … WebSep 4, 2014 · Maximizing k-Submodular Functions and Beyond. We consider the maximization problem in the value oracle model of functions defined on -tuples of sets …
WebDR-submodular continuous functions are important objectives with wide real-world applications spanning MAP inference in determinantal point processes (DPPs), and mean-field inference for probabilistic submodular models… WebData caching has been shown to be efficient in reducing network bandwidth consumption and accelerating information access. In a caching system, an important issue is coordinating data placement to achieve optimal system performance.
WebTowards Minimizing k -Submodular Functions Abstract. In this paper we investigate k -submodular functions. This natural family of discrete functions includes... Author …
WebTowards an Axiom System for Default Logic: Gerhard Lakemeyer, RWTH Aachen University ... Fast Semidifferential-based Submodular Function Optimization: Rishabh Iyer, University of Washington; et al. ... Energy-Minimizing Idle Listening in Wireless Networks: Xinyu Zhang & Kang G. Shin, University of Michigan: dr. david gazzaniga newport beach californiaWebMinimizing Polarization and Disagreement in Social Networks via Link Recommendation Liwang Zhu, ... Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components Nate Veldt, Austin R. Benson, ... K-Net: Towards Unified Image Segmentation Wenwei Zhang, Jiangmiao Pang, Kai Chen, ... energy smart pools softwareWebJan 1, 2024 · A k -submodular function is a generalization of submodular function, where the input consists of k disjoint subsets of the domain, instead of a single subset. The k … energy smart reserve power bpaWebToday's social platforms, such as Twitter and Facebook, continuously generate massive volumes of data. The resulting data streams exceed any reasonable limit for permanent storage, especially since data is often redundant, overlapping, sparse, and generally of low value. This calls for means to retain solely a small fraction of the data in an ... dr david gibbs on youtubeWebthe popular model of decomposable submodular functions. These are functions that can be written as sums of several “simple” submodular functions defined on small supports. Some definitions are needed to introduce our problem setting. Let f: 2V!R be a submodular function, and let n:= jVj. We can assume w.l.o.g. that f(;) = 0. dr david geier orthopedicWebTowards Understanding Theoretical Advantages of Complex-Reaction Networks. ... this paper presents the first universal algorithm for minimizing the adaptive regret of convex functions. ... Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms. energy smarts mechanical stockton njWebConvergence guarantees for kernel-based quadrature rules in misspecified settings Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu; Maximization of Approximately Submodular Functions Thibaut Horel, Yaron Singer; Causal meets Submodular: Subset Selection with Directed Information Yuxun Zhou, Costas J. Spanos dr. david gerst chicago il