1 code implementation • Control Engineering Practice 2024 • Xiaohan Chen, Rui Yang, Yihao Xue, Baoye Song, Zidong Wang
Recent advances in intelligent rotating machinery fault diagnosis have been enabled by the availability of massive labeled training data.
no code implementations • 16 Apr 2024 • Zhige Chen, Rui Yang, Mengjie Huang, Chengxuan Qin, Zidong Wang
Based on the presented issue, a novel bridging domain adaptation network (BDAN) is proposed, aiming to minimise the data distribution difference across sessions in the aspect of the electrode, thus improving and enhancing model performance.
no code implementations • 13 Apr 2024 • Zhihao Cao, Zidong Wang, Siwen Xie, Anji Liu, Lifeng Fan
Our findings illustrate the potential of AI-imbued assistive robots in improving the well-being of vulnerable groups.
no code implementations • 5 Mar 2024 • YaoDan Zhang, Zidong Wang, Ru Jia, Ru Li
Compared with the general metric learning model MetricF, the prediction error is reduced by 7. 29%.
2 code implementations • 19 Feb 2024 • Zeyu Lu, Zidong Wang, Di Huang, Chengyue Wu, Xihui Liu, Wanli Ouyang, Lei Bai
Nature is infinitely resolution-free.
1 code implementation • Computers in Biology and Medicine 2023 • Zhige Chen, Rui Yang, Mengjie Huang, Fumin Li, Guoping Lu, Zidong Wang
The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4. 02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios.
1 code implementation • IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023 • Chengxuan Qin, Rui Yang, Mengjie Huang, Weibo Liu, Zidong Wang
Moreover, SVG generates a uniform distribution and stabilizes the training process of models.
no code implementations • 16 Aug 2023 • Lei Guo, Wenshuo Li, Yukai Zhu, Xiang Yu, Zidong Wang
State estimation has long been a fundamental problem in signal processing and control areas.
no code implementations • 26 Jul 2023 • Xiang Huang, Zhuoyuan Li, Hongsheng Liu, Zidong Wang, Hongye Zhou, Bin Dong, Bei Hua
Recently, using neural networks to simulate spatio-temporal dynamics has received a lot of attention.
1 code implementation • ICCV 2023 • Yifan Pu, Yiru Wang, Zhuofan Xia, Yizeng Han, Yulin Wang, Weihao Gan, Zidong Wang, Shiji Song, Gao Huang
In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image.
Ranked #3 on Object Detection In Aerial Images on DOTA (using extra training data)
no code implementations • 9 Mar 2023 • Nchongmaje Ndipenoch, Alina Miron, Zidong Wang, Yongmin Li
Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely impair the central version.
no code implementations • 25 Feb 2023 • Nchongmaje Ndipenoch, Alina Miron, Zidong Wang, Yongmin Li
Retinal Optical Coherence Tomography (OCT), a noninvasive cross-sectional scan of the eye with qualitative 3D visualization of the retinal anatomy is use to study the retinal structure and the presence of pathogens.
1 code implementation • 31 Jan 2023 • Wendong Bi, Bingbing Xu, Xiaoqian Sun, Zidong Wang, HuaWei Shen, Xueqi Cheng
However, most nodes in the tribe-style graph lack attributes, making it difficult to directly adopt existing graph learning methods (e. g., Graph Neural Networks(GNNs)).
1 code implementation • journal 2023 • ZhiGang Liu, Xin Luo, Zidong Wang, Xiaohui Liu
Motivated by this discovery, this paper proposes a novel Constraintinduced Symmetric Nonnegative Matrix Factorization (C-SNMF) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent feature matrices, thus preserving its representation learning capacity; b) Incorporating a symmetry-regularizer into its objective function, which preserves the symmetry of the learnt low-rank approximation to the adjacency matrix, thereby making the resultant detector precisely illustrate the target network’s symmetry; and c) Introducing a graph-regularizer that preserves local invariance of the network’s intrinsic geometry into its learning objective, thus making the achieved detector well-aware of community structure within the target network.
2 code implementations • 20 Aug 2022 • Jun Zhang, Sirui Liu, Mengyun Chen, Haotian Chu, Min Wang, Zidong Wang, Jialiang Yu, Ningxi Ni, Fan Yu, Diqing Chen, Yi Isaac Yang, Boxin Xue, Lijiang Yang, YuAn Liu, Yi Qin Gao
Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development.
1 code implementation • Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence 2022 • Xiang Huang, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Min Wang, Haotian Chu, Jing Zhou, Fan Yu, Bei Hua, Bin Dong, Lei Chen
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems.
2 code implementations • 24 Jun 2022 • Sirui Liu, Jun Zhang, Haotian Chu, Min Wang, Boxin Xue, Ningxi Ni, Jialiang Yu, Yuhao Xie, Zhenyu Chen, Mengyun Chen, YuAn Liu, Piya Patra, Fan Xu, Jie Chen, Zidong Wang, Lijiang Yang, Fan Yu, Lei Chen, Yi Qin Gao
We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset.
no code implementations • 1 Apr 2022 • Jiahao Huang, Yingying Fang, Yang Nan, Huanjun Wu, Yinzhe Wu, Zhifan Gao, Yang Li, Zidong Wang, Pietro Lio, Daniel Rueckert, Yonina C. Eldar, Guang Yang
Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e. g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning.
no code implementations • 15 Nov 2021 • Xiang Huang, Zhanhong Ye, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Bingya Weng, Min Wang, Haotian Chu, Fan Yu, Bei Hua, Lei Chen, Bin Dong
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i. e., PDEs with different physical parameters, boundary conditions, shapes of computation domains, etc.
no code implementations • 2 Nov 2021 • Xiang Huang, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Bingya Weng, Min Wang, Haotian Chu, Jing Zhou, Fan Yu, Bei Hua, Lei Chen, Bin Dong
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems.
1 code implementation • CVPR 2021 • Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
1 code implementation • Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021 • Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
no code implementations • AAAI Technical Track on Machine Learning 2021 • Mengyun Chen, Kaixin Gao, Xiaolei Liu, Zidong Wang, Ningxi Ni, Qian Zhang, Lei Chen, Chao Ding, ZhengHai Huang, Min Wang, Shuangling Wang, Fan Yu, Xinyuan Zhao, Dachuan Xu
It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice.
no code implementations • 24 Dec 2020 • Zedong Tang, Fenlong Jiang, Junke Song, Maoguo Gong, Hao Li, Fan Yu, Zidong Wang, Min Wang
Optimizers that further adjust the scale of gradient, such as Adam, Natural Gradient (NG), etc., despite widely concerned and used by the community, are often found poor generalization performance, compared with Stochastic Gradient Descent (SGD).
no code implementations • 30 Nov 2020 • Tianze Gao, Huihui Pan, Zidong Wang, Huijun Gao
In this paper, a conditional random field (CRF) based framework is put forward to tackle the tracklet inactivation issue in online MOT problems.
no code implementations • 27 Nov 2020 • Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang, Min Wang, Shuangling Wang, Zidong Wang, Dachuan Xu, Fan Yu
Using second-order optimization methods for training deep neural networks (DNNs) has attracted many researchers.
no code implementations • 21 Nov 2020 • Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang, Min Wang, Zidong Wang, Dachuan Xu, Fan Yu
There have been many attempts to use second-order optimization methods for training deep neural networks.