most citedNon-convex approaches for low-rank tensor completion under tubal sampling

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2023

Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization

Russell Alan Hart, Linlin Yu, Yifei Lou +1

Deep neural networks have achieved significant success in the last decades, but they are not well-calibrated and often produce unreliable predictions. A large number of literature…

math.NA2023

Minimizing Quotient Regularization Model

Chao Wang, Jean-Francois Aujol, Guy Gilboa +1

Quotient regularization models (QRMs) are a class of powerful regularization techniques that have gained considerable attention in recent years, due to their ability to handle comp…

cs.LG2023★ 1 cited

Non-convex approaches for low-rank tensor completion under tubal sampling

Zheng Tan, Longxiu Huang, HanQin Cai +1

Tensor completion is an important problem in modern data analysis. In this work, we investigate a specific sampling strategy, referred to as tubal sampling. We propose two novel no…