Results 31 to 40 of about 96,100 (273)
Tensor Completion Methods for Collaborative Intelligence
IEEE Access, 2020 In the race to bring Artificial Intelligence (AI) to the edge, collaborative intelligence has emerged as a promising way to lighten the computation load on edge devices that run applications based on Deep Neural Networks (DNNs).
Lior Bragilevsky, Ivan V. Bajic
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Rank revealing‐based tensor completion using improved generalized tensor multi‐rank minimization
IET Signal Processing, 2021 The authors address the problem of tensor completion from limited samplings. An improved generalized tubal Kronecker decomposition is first proposed to reveal the tensor structure of the targeted data, and the improved generalized tensor tubal‐rank and ...
Wei Z. Sun, Peng Zhang, Bo Zhao
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Accelerated non-negative tensor completion via integer programming
Frontiers in Applied Mathematics and Statistics, 2023 The problem of tensor completion has applications in healthcare, computer vision, and other domains. However, past approaches to tensor completion have faced a tension in that they either have polynomial-time computation but require exponentially more ...
Wenhao Pan +3 more
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Matrix completion and tensor rank [PDF]
Linear and Multilinear Algebra, 2015 In this paper, we show that the low rank matrix completion problem can be reduced to the problem of finding the rank of a certain tensor.
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Provable Tensor-Train Format Tensor Completion by Riemannian Optimization
, 2021 The tensor train (TT) format enjoys appealing advantages in handling structural high-order tensors. The recent decade has witnessed the wide applications of TT-format tensors from diverse disciplines, among which tensor completion has drawn considerable attention.
Cai, Jianfeng, Li, Jingyang, Xia, Dong
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Deterministic Tensor Completion with Hypergraph Expanders
SIAM Journal on Mathematics of Data Science, 2021 We provide a novel analysis of low-rank tensor completion based on hypergraph expanders. As a proxy for rank, we minimize the max-quasinorm of the tensor, which generalizes the max-norm for matrices. Our analysis is deterministic and shows that the number of samples required to approximately recover an order-$t$ tensor with at most $n$ entries per ...
Kameron Decker Harris, Yizhe Zhu
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Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods
IEEE Access, 2020 Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A handful of completion methods have been proposed, among which the tensor-based models have been shown to be the most advantageous for missing traffic data ...
Qin Li +4 more
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Higher level affine Schur and Hecke algebras [PDF]
, 2018 We define a higher level version of the affine Hecke algebra and prove that, after completion, this algebra is isomorphic to a completion of Webster's tensor product algebra of type A.
Maksimau, Ruslan, Stroppel, Catharina
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Traffic Data Restoration Method Based on Tensor Weighting and Truncated Nuclear Norm [PDF]
Jisuanji kexue, 2023 The problem of missing data seriously affects a series of activities in intelligent transportation systems,such as monitoring traffic dynamics,predicting traffic flow,and deploying traffic planning through data.Therefore,a traffic flow data ...
WU Jiangnan, ZHANG Hongmei, ZHAO Yongmei, ZENG Hang, HU Gang
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Cross: Efficient low-rank tensor completion [PDF]
The Annals of Statistics, 2019 The completion of tensors, or high-order arrays, attracts significant attention in recent research. Current literature on tensor completion primarily focuses on recovery from a set of uniformly randomly measured entries, and the required number of measurements to achieve recovery is not guaranteed to be optimal.
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