Results 11 to 20 of about 6,254,702 (218)
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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Dynamical low-rank training of neural networks [PDF]
, 2022 openNeural networks have achieved tremendous success in a large variety of applications. However, their space and time computational demand can limit their usage in resource limited devices. At the same time, overparametrization seems to be necessary in
ZANGRANDO, EMANUELE
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CoRR, 2021 The latent space of a Generative Adversarial Network (GAN) has been shown to encode rich semantics within some subspaces. To identify these subspaces, researchers typically analyze the statistical information from a collection of synthesized data, and the identified subspaces tend to control image attributes globally (i.e., manipulating an attribute ...
Jiapeng Zhu 0001 +6 more
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Journal of Mathematical Physics, 2023 We present a method to generate all fusion rings of a specific rank and multiplicity. This method generated exhaustive lists of fusion rings up to order 9 for several multiplicities. We introduce a class of non-commutative fusion rings based on a group with transitive action on a set. This construction generalises the Tambara–Yamagami (TY) and Haagerup-
G. Vercleyen, J. K. Slingerland
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Low-Rank Methods for Radiation Transport Calculations
, 2023 This dissertation seeks to reduce the computational cost in radiation transport calculations using dynamical low-rank approximation (DLR) methods, a complexity reduction technique to approximate a tensor or a matrix with a reduced rank.
Zhuogang Peng (17675484)
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The Annals of Statistics, 2023 This paper studies inference in linear models with a high-dimensional parameter matrix that can be well-approximated by a ``spiked low-rank matrix.'' A spiked low-rank matrix has rank that grows slowly compared to its dimensions and nonzero singular values that diverge to infinity.
Chernozhukov, Victor +3 more
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Low-Rank Approximation of Tensors [PDF]
, 2015 28 pages, 5 ...
Friedland, Shmuel, Tammali, Venu
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Low rank multivariate regression [PDF]
Electronic Journal of Statistics, 2011 We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non ...
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CoRRThe goal in thinning is to summarize a dataset using a small set of representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel Halving and Compress can match the quality of uniform subsampling while substantially reducing the number of summary points.
Annabelle Michael Carrell +4 more
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