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Found 8 papers, 2 papers with code
Prompt-based Grouping Transformer for Nucleus Detection and Classification
Automatic nuclei detection and classification can produce effective information for disease diagnosis.
Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration
Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scale data analysis.
Latent Matrices for Tensor Network Decomposition and to Tensor Completion
The prevalent fully-connected tensor network (FCTN) has achieved excellent success to compress data.
A high-order tensor completion algorithm based on Fully-Connected Tensor Network weighted optimization
The algorithm performs a composition of the completed tensor by initialising the factors from the FCTN decomposition.
Bayesian Robust Tensor Ring Model for Incomplete Multiway Data
Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observation with outlier corruption.
Fast Hypergraph Regularized Nonnegative Tensor Ring Factorization Based on Low-Rank Approximation
For the high dimensional data representation, nonnegative tensor ring (NTR) decomposition equipped with manifold learning has become a promising model to exploit the multi-dimensional structure and extract the feature from tensor data.
Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data
To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF).
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.
