Results 231 to 240 of about 15,373 (263)
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Proceedings of the International Congress of Mathematicians 2010 (ICM 2010), 2011
Let G be a semisimple connected complex algebraic group. We study the tensor product decomposition of irreducible finite-dimensional representations of G. The techniques we employ range from representation theory to algebraic geometry and topology. This is mainly a survey of author’s various results on the subject obtained individually or jointly with ...
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Let G be a semisimple connected complex algebraic group. We study the tensor product decomposition of irreducible finite-dimensional representations of G. The techniques we employ range from representation theory to algebraic geometry and topology. This is mainly a survey of author’s various results on the subject obtained individually or jointly with ...
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Nonnegative Tensor Decomposition
2013It is more and more common to encounter applications where the collected data is most naturally stored or represented in a multi-dimensional array, known as a tensor. The goal is often to approximate this tensor as a sum of some type of combination of basic elements, where the notation of what is a basic element is specific to the type of factorization
N. Hao, L. Horesh, M. E. Kilmer
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SIAM Journal on Scientific Computing, 2011
A simple nonrecursive form of the tensor decomposition in $d$ dimensions is presented. It does not inherently suffer from the curse of dimensionality, it has asymptotically the same number of parameters as the canonical decomposition, but it is stable and its computation is based on low-rank approximation of auxiliary unfolding matrices.
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A simple nonrecursive form of the tensor decomposition in $d$ dimensions is presented. It does not inherently suffer from the curse of dimensionality, it has asymptotically the same number of parameters as the canonical decomposition, but it is stable and its computation is based on low-rank approximation of auxiliary unfolding matrices.
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Facial Recognition Using Tensor-Tensor Decompositions
SIAM Journal on Imaging Sciences, 2013A tensor is a multidimensional array. First-order tensors and second-order tensors can be viewed as vectors and matrices, respectively. Tensors of higher order, with the ability to include more information, appear more frequently nowadays in image and signal processing, data mining, biomedical engineering, and so on.
Ning Hao +3 more
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Tensor Decomposition of Cooperative Games
SIAM Journal on Applied Mathematics, 1975A decomposition theory for n-person games is introduced. A “unique factorization” theorem is proved. In general, every monotonic game has a unique decomposition with a quotient that is either prime or absolutely decomposable. Finally, an application to reliability theory is suggested.
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Functional Tensor Singular Value Decomposition
SIAM Journal on Scientific ComputingzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chuan Wang +4 more
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Parallel convolutional processing using an integrated photonic tensor core
Nature, 2021Johannes Feldmann, Nathan Youngblood
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Tensor lattice field theory for renormalization and quantum computing
Reviews of Modern Physics, 2022Yannick Meurice +2 more
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Third-order nonlinear Hall effect induced by the Berry-connection polarizability tensor
Nature Nanotechnology, 2021Shen Lai, Huiying Liu, Zhaowei Zhang
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