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Found 6 papers, 2 papers with code
Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation
To the best of our knowledge, this is the first study to incorporate the randomized low-rank approximation into the RHTC problem.
Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
Recent research have made significant progress by adopting two insightful tensor priors, i. e., global low-rankness (L) and local smoothness (S) across different tensor modes, which are always encoded as a sum of two separate regularization terms into the recovery models.
VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges
Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years.
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
The model-based methods have good generalization ability, while the runtime cannot meet the fast processing requirements of the practical situations due to the large size of an HSI data $ \mathbf{X} \in \mathbb{R}^{MN\times B}$.
Generalized Nonconvex Approach for Low-Tubal-Rank Tensor Recovery
The tensor-tensor product-induced tensor nuclear norm (t-TNN) (Lu et al., 2020) minimization for low-tubal-rank tensor recovery attracts broad attention recently.
Deep Neural Network Based Relation Extraction: An Overview
One of the representations of knowledge is semantic relations between entities.
