no code implementations • 23 Jan 2024 • Jiayi Xu, Shihua Fu, Liyuan Xia, Jianjun Wang
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.
no code implementations • 19 May 2023 • Wenjin Qin, Hailin Wang, Feng Zhang, Weijun Ma, Jianjun Wang, TingWen Huang
To the best of our knowledge, this is the first study to incorporate the randomized low-rank approximation into the RHTC problem.
1 code implementation • 4 Feb 2023 • Hailin Wang, Jiangjun Peng, Wenjin Qin, Jianjun Wang, Deyu Meng
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.
1 code implementation • CVPR 2023 • Bin Fu, Junjun He, Jianjun Wang, Yu Qiao
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.
1 code implementation • IEEE Transactions on Neural Networks and Learning Systems 2022 • Hailin Wang, Feng Zhang, Jianjun Wang, TingWen Huang, Jianwen Huang, and Xinling Liu
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.
no code implementations • 29 Jan 2022 • Jiangjun Peng, Yao Wang, Hongying Zhang, Jianjun Wang, Deyu Meng
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.
no code implementations • 4 Jun 2019 • Feng Zhang, Wendong Wang, Jingyao Hou, Jianjun Wang, Jianwen Huang
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.