no code implementations • 19 Apr 2024 • Xinyu Liu, Hai Zhang
Second, we reveal that there exists a fundamental limit to the problem of estimating the number of Gaussian components or model order in the mixture model if the number of i. i. d samples is finite.
no code implementations • 4 Feb 2024 • Lanqing Li, Hai Zhang, Xinyu Zhang, Shatong Zhu, Junqiao Zhao, Pheng-Ann Heng
As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely.
1 code implementation • 21 Dec 2023 • Hai Zhang, Chunwei Wu, Guitao Cao, Hailing Wang, Wenming Cao
Editing real images authentically while also achieving cross-domain editing remains a challenge.
1 code implementation • 30 Nov 2023 • Hai Zhang, Junzhe Xu, Shanlin Jiang, Zhenan He
This limitation severely constrains the potential of semantics in Few-Shot Learning.
no code implementations • 27 Oct 2023 • Zetao Fei, Hai Zhang
Moreover, in terms of speed, their performance is comparable to the state-of-the-art algorithms, while being more reliable for reconstructing line spectra with cluster structure.
no code implementations • 24 Jun 2023 • Xiao Zhang, Hai Zhang, Hongtu Zhou, Chang Huang, Di Zhang, Chen Ye, Junqiao Zhao
In this paper, we propose a method to construct a boundary that discriminates safe and unsafe states.
no code implementations • 12 Mar 2023 • Zetao Fei, Hai Zhang
The new feature also allows for a subsampling strategy that can circumvent the computation of singular-value decomposition for large matrices as in the usual subspace methods.
no code implementations • 1 Apr 2022 • Ping Liu, Hai Zhang
We consider the problem of resolving closely spaced point sources in one dimension from their Fourier data in a bounded domain.
no code implementations • 5 Apr 2021 • Cheng Xue, Lei Zhu, Huazhu Fu, Xiaowei Hu, Xiaomeng Li, Hai Zhang, Pheng Ann Heng
The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement.
no code implementations • 22 Mar 2021 • Ping Liu, Hai Zhang
Our results indicate that there exists a phase transition phenomenon regarding to the super-resolution factor and the signal-to-noise ratio in each of the two recovery problems.
no code implementations • 22 Jan 2021 • Puyu Wang, Yunwen Lei, Yiming Ying, Hai Zhang
We significantly relax these restrictive assumptions and establish privacy and generalization (utility) guarantees for private SGD algorithms using output and gradient perturbations associated with non-smooth convex losses.
no code implementations • 15 Jan 2021 • Junshan Lin, Hai Zhang
The main focus is on the existence and stability of interface modes that are induced by topological properties of the bulk structure.
Band Gap Mathematical Physics Mathematical Physics
no code implementations • 25 Apr 2020 • Xiao Guo, Yixuan Qiu, Hai Zhang, Xiangyu Chang
Directed networks are broadly used to represent asymmetric relationships among units.
no code implementations • 20 Jan 2020 • Hai Zhang, Xiao Guo, Xiangyu Chang
In this paper, we study the spectral clustering using randomized sketching algorithms from a statistical perspective, where we typically assume the network data are generated from a stochastic block model that is not necessarily of full rank.
no code implementations • MDPI Remote Sensing 2020 • Jianhao Gao, Qiangqiang Yuan, Jie Li, Hai Zhang, Xin Su
The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function.
Ranked #6 on Cloud Removal on SEN12MS-CR
no code implementations • 6 Sep 2019 • Wenqing Su, Xiao Guo, Hai Zhang
In this paper, we study the problem of precision matrix estimation when the dataset contains sensitive information.
no code implementations • 2 Aug 2019 • Puyu Wang, Hai Zhang
By the property of the post-processing holding of differential privacy, the proposed approach satisfies the $\epsilon-$differential privacy even when the original problem is unstable.
no code implementations • 11 Apr 2019 • Yun Jiang, Ning Tan, Tingting Peng, Hai Zhang
In the proposed D-Net, the dilation convolution is used in the backbone network to obtain a larger receptive field without losing spatial resolution, so as to reduce the loss of feature information and to reduce the difficulty of tiny thin vessels segmentation.