Zenglin Xu
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Explore the profile of Zenglin Xu including associated specialties, affiliations and a list of published articles.
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32
Citations
82
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Recent Articles
1.
Zeng D, Hu X, Liu S, Yu Y, Wang Q, Xu Z
Neural Netw
. 2025 Mar;
187:107278.
PMID: 40056825
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federated learning systems, these data samples are often decentralized...
2.
Wu Z, Xu Z, Zeng D, Wang Q, Liu J
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40030808
Federated learning (FL) has surged in prominence due to its capability of collaborative model training without direct data sharing. However, the vast disparity in local data distributions among clients, often...
3.
Li R, Xu Z, Xu J, Pan X, Wu H, Huang X, et al.
Int J Med Inform
. 2024 Mar;
186:105425.
PMID: 38554589
Objective: For patients in the Intensive Care Unit (ICU), the timing of intubation has a significant association with patients' outcomes. However, accurate prediction of the timing of intubation remains an...
4.
Wen L, Wang X, Liu J, Xu Z
IEEE Trans Pattern Anal Mach Intell
. 2024 Mar;
46(9):6097-6108.
PMID: 38512732
Self-supervised learning aims to learn representation that can be effectively generalized to downstream tasks. Many self-supervised approaches regard two views of an image as both the input and the self-supervised...
5.
Xu J, Pan X, Wang J, Pei W, Liao Q, Xu Z
IEEE Trans Neural Netw Learn Syst
. 2024 Feb;
36(2):3098-3110.
PMID: 38393839
Few-shot classification aims to adapt classifiers trained on base classes to novel classes with a few shots. However, the limited amount of training data is often inadequate to represent the...
6.
Zhuge R, Wang J, Xu Z, Xu Y
Neural Netw
. 2023 Sep;
168:313-325.
PMID: 37776616
Recent Transformer-based networks have shown impressive performance on single image denoising tasks. While the Transformer model promotes the interaction of long-range features, it generally involves high computational complexity. In this...
7.
He L, Ai Q, Yang X, Ren Y, Wang Q, Xu Z
Neural Netw
. 2023 Sep;
167:706-714.
PMID: 37729786
Adversarial training is considered one of the most effective methods to improve the adversarial robustness of deep neural networks. Despite the success, it still suffers from unsatisfactory performance and overfitting....
8.
Wang C, Gao S, Wang P, Gao C, Pei W, Pan L, et al.
IEEE Trans Neural Netw Learn Syst
. 2022 Oct;
35(5):6963-6975.
PMID: 36279339
Real-world data usually present long-tailed distributions. Training on imbalanced data tends to render neural networks perform well on head classes while much worse on tail classes. The severe sparseness of...
9.
Huang S, Tsang I, Xu Z, Lv J
IEEE Trans Neural Netw Learn Syst
. 2022 Sep;
35(3):4206-4219.
PMID: 36136919
Multiview graph clustering has emerged as an important yet challenging technique due to the difficulty of exploiting the similarity relationships among multiple views. Typically, the similarity graph for each view...
10.
Pan X, Xu J, Pan Y, Wen L, Lin W, Bai K, et al.
Neural Netw
. 2022 Sep;
155:360-368.
PMID: 36115162
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Residual-like networks, such as ResNets, mainly focus on the skip connection to avoid...