Results 11 to 20 of about 2,699,353 (198)
Deep Compressed Sensing for Learning Submodular Functions [PDF]
Sensors, 2020 The AI community has been paying attention to submodular functions due to their various applications (e.g., target search and 3D mapping). Learning submodular functions is a challenge since the number of a function’s outcomes of N sets is 2 N ...
Yu-Chung Tsai, Kuo-Shih Tseng
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Ranking with submodular functions on a budget. [PDF]
Data Min Knowl Discov, 2022 AbstractSubmodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted in the context of selecting a set of items.
Zhang G, Tatti N, Gionis A.
europepmc +7 more sources
Hypergraphs with edge-dependent vertex weights: p-Laplacians and spectral clustering [PDF]
Frontiers in Big Data, 2023 We study p-Laplacians and spectral clustering for a recently proposed hypergraph model that incorporates edge-dependent vertex weights (EDVW). These weights can reflect different importance of vertices within a hyperedge, thus conferring the hypergraph ...
Yu Zhu, Santiago Segarra
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Learning submodular functions [PDF]
Proceedings of the forty-third annual ACM symposium on Theory of computing, 2011 There has been much interest in the machine learning and algorithmic game theory communities on understanding and using submodular functions. Despite this substantial interest, little is known about their learnability from data. Motivated by applications, such as pricing goods in economics, this paper considers PAC-style learning of submodular ...
Balcan, Maria-Florina +1 more
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Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks [PDF]
Scientific Reports, 2022 Selecting diverse molecules from unexplored areas of chemical space is one of the most important tasks for discovering novel molecules and reactions. This paper proposes a new approach for selecting a subset of diverse molecules from a given molecular ...
Tomohiro Nakamura +5 more
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Sparsification of Decomposable Submodular Functions
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Submodular functions are at the core of many machine learning and data mining tasks. The underlying submodular functions for many of these tasks are decomposable, i.e., they are sum of several simple submodular functions. In many data intensive applications, however, the number of underlying submodular functions in the original function is so large ...
Akbar Rafiey, Yuichi Yoshida
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Horn functions and submodular boolean functions [PDF]
Theoretical Computer Science, 1997 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Oya Ekin, Peter L. Hammer, Uri N. Peled
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Discovering Key Sub-Trajectories to Explain Traffic Prediction [PDF]
Sensors, 2022 Flow prediction has attracted extensive research attention; however, achieving reliable efficiency and interpretability from a unified model remains a challenging problem.
Hongjun Wang +4 more
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Concave Aspects of Submodular Functions [PDF]
2020 IEEE International Symposium on Information Theory (ISIT), 2020 Submodular Functions are a special class of set functions, which generalize several information-theoretic quantities such as entropy and mutual information [1]. Submodular functions have subgradients and subdifferentials [2] and admit polynomial-time algorithms for minimization, both of which are fundamental characteristics of convex functions ...
Rishabh Iyer
exaly +5 more sources
Maximizing Symmetric Submodular Functions [PDF]
ACM Transactions on Algorithms, 2015 Symmetric submodular functions are an important family of submodular functions capturing many interesting cases, including cut functions of graphs and hypergraphs. Maximization of such functions subject to various constraints receives little attention by current research, unlike similar minimization problems that have been widely studied. In this work,
Moran Feldman
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