Results 31 to 40 of about 1,077,071 (292)
A Hybrid Norm for Guaranteed Tensor Recovery
Frontiers in Physics, 2022 Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and
Yihao Luo +5 more
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Tucker Tensor Decomposition on FPGA [PDF]
2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019 Tensor computation has emerged as a powerful mathematical tool for solving high-dimensional and/or extreme-scale problems in science and engineering. The last decade has witnessed tremendous advancement of tensor computation and its applications in machine learning and big data. However, its hardware optimization on resource-constrained devices remains
Zhang, Kaiqi +2 more
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ADA-Tucker: Compressing deep neural networks via adaptive dimension adjustment tucker decomposition [PDF]
Neural Networks, 2019 25 pages, 12 ...
Zhisheng Zhong +3 more
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Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
Applied Sciences, 2022 For the purpose of solving the large temperature field reconstruction error caused by different measuring point arrangements and the problem that the prior dataset cannot be built due to data loss or distortion in actual measurement, a three-dimensional ...
Zhaoyu Liu +3 more
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Discovering facts with boolean tensor tucker decomposition [PDF]
Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013 Open Information Extraction (Open IE) has gained increasing research interest in recent years. The first step in Open IE is to extract raw subject--predicate--object triples from the data. These raw triples are rarely usable per se, and need additional post-processing.
Erdős, D., Miettinen, P.
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Remote Sensing, 2022 To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to improve
Tong Gao, Hao Chen, Junhong Lu
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Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness [PDF]
IEEE Transactions on Image Processing, 2015 appears in IEEE Transactions on Image Processing ...
Zhou, Guoxu +3 more
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Tucker Decomposition Based on a Tensor Train of Coupled and Constrained CP Cores
IEEE Signal Processing Letters, 2023 Many real-life signal-based applications use the Tucker decomposition of a high dimensional/order tensor. A well-known problem with the Tucker model is that its number of entries increases exponentially with its order, a phenomenon known as the “curse of
Maxence Giraud +4 more
semanticscholar +1 more source
Hyperspectral image, video compression using sparse tucker tensor decomposition
IET Image Processing, 2021 Hyperspectral image and videos provide rich spectral information content, which facilitates accurate classification, unmixing, temporal change detection, and so on.
Samiran Das
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A Two-Stage Framework for Directed Hypergraph Link Prediction
Mathematics, 2022 Hypergraphs, as a special type of graph, can be leveraged to better model relationships among multiple entities. In this article, we focus on the task of hyperlink prediction in directed hypergraphs, which finds a wide spectrum of applications in ...
Guanchen Xiao +4 more
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