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Found 4 papers, 0 papers with code
An Efficient Tensor Completion Method via New Latent Nuclear Norm
In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding scheme.
Tensor-Ring Nuclear Norm Minimization and Application for Visual Data Completion
Tensor ring (TR) decomposition has been successfully used to obtain the state-of-the-art performance in the visual data completion problem.
Low-Rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling
We demonstrate this by compressing the convolutional layers via randomly-shuffled tensor decomposition (RsTD) for a standard classification task using CIFAR-10.
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing more localized, less overlapped feature representations than other variants of NMF while keeping satisfactory fit to data.
