Search Results for author:
Found 7 papers, 3 papers with code
Minimum observability of probabilistic Boolean networks
This paper studies the minimum observability of probabilistic Boolean networks (PBNs), the main objective of which is to add the fewest measurements to make an unobservable PBN become observable.
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.
Neural Transformation Fields for Arbitrary-Styled Font Generation
Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values.
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.
Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices
It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques.
Tensor Restricted Isometry Property Analysis For a Large Class of Random Measurement Ensembles
In previous work, theoretical analysis based on the tensor Restricted Isometry Property (t-RIP) established the robust recovery guarantees of a low-tubal-rank tensor.
