2 citations · 4 across the 6 of their papers we have counts for
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cs.LG2022★ 2 cited
Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration
Yichun Qiu, Weijun Sun, Guoxu Zhou +1
Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scale data analysis. Randomized tensor decompositions have emerged as powerful tools…
cs.LG2022★ 1 cited
Latent Matrices for Tensor Network Decomposition and to Tensor Completion
Peilin Yang, Weijun Sun, Qibin Zhao +1
The prevalent fully-connected tensor network (FCTN) has achieved excellent success to compress data. However, the FCTN decomposition suffers from slow computational speed when faci…
cs.LG2022
A high-order tensor completion algorithm based on Fully-Connected Tensor Network weighted optimization
Peilin Yang, Yonghui Huang, Yuning Qiu +2
Tensor completion aimes at recovering missing data, and it is one of the popular concerns in deep learning and signal processing. Among the higher-order tensor decomposition algori…