Results 31 to 40 of about 9,171,861 (296)
Algorithms for $\ell_p$ Low Rank Approximation
CoRR, 2017 To appear in ...
Flavio Chierichetti +5 more
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Low rank approximation in simulations of quantum algorithms [PDF]
Journal of Computational Science, 2022 Simulating quantum algorithms on classical computers is challenging when the system size, i.e., the number of qubits used in the quantum algorithm, is moderately large. However, some quantum algorithms and the corresponding quantum circuits can be simulated efficiently on a classical computer if the input quantum state is a low-rank tensor and all ...
Linjian Ma, Chao Yang 0001
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Deep compression of convolutional neural networks with low‐rank approximation
ETRI Journal, 2018 The application of deep neural networks (DNNs) to connect the world with cyber physical systems (CPSs) has attracted much attention. However, DNNs require a large amount of memory and computational cost, which hinders their use in the relatively low‐end ...
Marcella Astrid, Seung‐Ik Lee
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Image Denoising via Nonlocal Low Rank Approximation With Local Structure Preserving
IEEE Access, 2019 The nuclear norm minimization method emerged from a patch-based low-rank model leads to an excellent image denoising performance, where the non-local self-similarity over image patches is exploited.
Zhou Liu, Lei Yu, Hong Sun
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Low-Rank Approximation of Frequency Response Analysis of Perforated Cylinders under Uncertainty
Applied Sciences, 2022 Frequency response analysis under uncertainty is computationally expensive. Low-rank approximation techniques can significantly reduce the solution times.
Harri Hakula, Mikael Laaksonen
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ALORA: Affine Low-Rank Approximations
Journal of Scientific Computing, 2018 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ayala, Alan +2 more
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The quantum low-rank approximation problem
CoRR, 2022 9 pages, 1 ...
Nic Ezzell +2 more
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Low rank prior in single patches for non-pointwise impulse noise removal [PDF]
, 2015 This paper introduces a low rank prior in small oriented noise-free image patches: Considering an oriented patch as a matrix, a low-rank matrix approximation is enough to preserve the texture details in the optimally oriented patch.
Trucco, Emanuele; id_orcid +2 more
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On Approximation Algorithm for Orthogonal Low-Rank Tensor Approximation
Journal of Optimization Theory and Applications, 2022 The goal of this work is to fill a gap in [Yang, SIAM J. Matrix Anal. Appl, 41 (2020), 1797--1825]. In that work, an approximation procedure was proposed for orthogonal low-rank tensor approximation; however, the approximation lower bound was only established when the number of orthonormal factors is one.
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Introducing a New Hybrid Adaptive Local Optimal Low Rank Approximation Method for Denoising Images [PDF]
International Journal of Industrial Electronics, Control and Optimization, 2020 This paper aimed to formulate image noise reduction as an optimization problem and denoise the target image using matrix low rank approximation. Considering the fact that the smaller pieces of an image are more similar (more dependent) in natural images;
sadegh kalantari +2 more
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