Results 31 to 40 of about 1,516 (207)
Geometric Primitive-Guided UAV Path Planning for High-Quality Image-Based Reconstruction
Remote Sensing, 2023 Image-based refined 3D reconstruction relies on high-resolution and multi-angle images of scenes. The assistance of multi-rotor drones and gimbal provides great convenience for image acquisition.
Hao Zhou +7 more
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Hardness of submodular cost allocation : lattice matching and a simplex coloring conjecture [PDF]
, 2014 We consider the Minimum Submodular Cost Allocation (MSCA) problem. In this problem, we are given k submodular cost functions f1, ... , fk: 2V -> R+ and the goal is to partition V into k sets A1, ..., Ak so as to minimize the total cost sumi = 1,k fi(Ai).
Vondrák, Jan, Ene, Alina
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Monotone Submodular Maximization over a Matroid via Non-Oblivious Local Search [PDF]
, 2013 We present an optimal, combinatorial 1−1/e approximation algorithm for monotone submodular optimization over a matroid constraint. Compared to the continuous greedy algorithm (Calinescu, Chekuri, Pál and Vondrák, 2008), our algorithm is extremely simple ...
Filmus, Yuval +3 more
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Submodular stochastic probing on matroids [PDF]
, 2014 In a stochastic probing problem we are given a universe E, where each element e in E is active independently with probability p in [0,1], and only a probe of e can tell us whether it is active or not. On this universe we execute a process that one by one
Sviridenko, Maxim +5 more
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Survey of Automatic Labeling Methods for Topic Models [PDF]
Jisuanji kexue yu tansuo, 2023 Topic models are often used in modeling unstructured corpora and discrete data to extract the latent topic. As topics are generally expressed in the form of word lists, it is usually difficult for users to understand the meanings of topics, especially ...
HE Dongbin, TAO Sha, ZHU Yanhong, REN Yanzhao, CHU Yunxia
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IEEE Access, 2018 Submodular optimization plays a significant role in combinatorial problems, since it captures the structure of the edge cuts in graphs, the coverage of sets, and so on. Many data mining and machine learning problems can be cast as submodular maximization
Qilian Yu, Li Xu, Shuguang Cui
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Submodular Optimization in the MapReduce Model
CoRR, 2018 Submodular optimization has received significant attention in both practice and theory, as a wide array of problems in machine learning, auction theory, and combinatorial optimization have submodular structure. In practice, these problems often involve large amounts of data, and must be solved in a distributed way.
Liu, Paul, Vondrak, Jan
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Stochastic Block-Coordinate Gradient Projection Algorithms for Submodular Maximization
Complexity, 2018 We consider a stochastic continuous submodular huge-scale optimization problem, which arises naturally in many applications such as machine learning.
Zhigang Li +5 more
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Submodular Optimization Approach for Entity Summarization in Knowledge Graph Driven by Large Language Models [PDF]
Jisuanji kexue yu tansuoThe continuous expansion of the knowledge graph has made entity summarization a research hotspot. The goal of entity summarization is to obtain a brief description of an entity from large-scale triple-structured facts that describe it.
ZHANG Qi, ZHONG Hao
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Optimal Distributed Submodular Optimization via Sketching [PDF]
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018 We present distributed algorithms for several classes of submodular optimization problems such as k-cover, set cover, facility location, and probabilistic coverage. The new algorithms enjoy almost optimal space complexity, optimal approximation guarantees, optimal communication complexity (and run in only four rounds of computation), addressing major ...
MohammadHossein Bateni +2 more
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