Results 31 to 40 of about 2,491 (218)
On multistage learning a hidden hypergraph [PDF]
2016 IEEE International Symposium on Information Theory (ISIT), 2016 5 pages, IEEE ...
Arkadii G. D'yachkov +3 more
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Learning Low Degree Hypergraphs
CoRR, 2022 We study the problem of learning a hypergraph via edge detecting queries. In this problem, a learner queries subsets of vertices of a hidden hypergraph and observes whether these subsets contain an edge or not. In general, learning a hypergraph with $m$ edges of maximum size $d$ requires $Ω((2m/d)^{d/2})$ queries.
Eric Balkanski +2 more
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Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image
Remote Sensing, 2021 Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information.
Jinhuan Xu, Liang Xiao, Jingxiang Yang
doaj +1 more source
Deep Hypergraph Structure Learning
CoRR, 2022 Learning on high-order correlation has shown superiority in data representation learning, where hypergraph has been widely used in recent decades. The performance of hypergraph-based representation learning methods, such as hypergraph neural networks, highly depends on the quality of the hypergraph structure.
Zizhao Zhang 0003 +3 more
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, 2021 Artificial intelligence (AI) based drug design has demonstrated great potential to fundamentally change the pharmaceutical industries. Currently, a key issue in AI-based drug design is efficient transferable molecular descriptors or fingerprints.
Xia, Kelin
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Dynamic Hypergraph Structure Learning [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 In recent years, hypergraph modeling has shown its superiority on correlation formulation among samples and has wide applications in classification, retrieval, and other tasks. In all these works, the performance of hypergraph learning highly depends on the generated hypergraph structure.
Zizhao Zhang 0003 +2 more
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Edge Representation Learning with Hypergraphs
CoRR, 2021 NeurIPS ...
Jaehyeong Jo +5 more
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Review on graph learning for dimensionality reduction of hyperspectral image
Geo-spatial Information Science, 2020 Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction
Liangpei Zhang, Fulin Luo
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Learning Causal Effects on Hypergraphs
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022 Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existing studies which leverage statistical dependencies, we study hypergraphs from the perspective of causality. Specifically, in this paper, we focus on the problem of individual
Jing Ma 0002 +5 more
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The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
CoRR, 2013 Hypergraphs allow one to encode higher-order relationships in data and are thus a very flexible modeling tool. Current learning methods are either based on approximations of the hypergraphs via graphs or on tensor methods which are only applicable under special conditions.
M. Hein +3 more
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