This paper proposes and analyzes data synchronization techniques that not only resynchronize after encoded bits are corrupted by insertions, deletions or substitution errors, but also produce estimates of the time indices of the decoded data.

Recent studies have discovered that deep networks are capable of memorizing the entire data even when the labels are completely random. Since deep models are trained on big data where labels are often noisy, the ability to overfit noise can lead to poor performance. To overcome the overfitting on corrupted training data, we propose a novel technique to regularize deep networks in the data dimen...

The efficient estimation of an approximate model order is very important for real applications with multi-dimensional data if the observed low-rank corrupted by additive noise. In this paper, we present a novel robust method noise-corrupted based on LineAr Regression Global Eigenvalues (LaRGE). LaRGE uses multi-linear singular values obtained from HOSVD measurement tensor to construct global ei...

Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image ...