1 code implementation • 21 Mar 2024 • Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao, Jie Jiang
In this paper, we uncover a new challenge associated with BCE loss in scenarios with sparse positive feedback, such as CTR prediction: the gradient vanishing for negative samples.
no code implementations • 7 Nov 2023 • Chenwei Tang, Wenqiang Zhou, Dong Wang, Caiyang Yu, Zhenan He, Jizhe Zhou, Shudong Huang, Yi Gao, Jianming Chen, Wentao Feng, Jiancheng Lv
The advent of Industry 4. 0 has precipitated the incorporation of Artificial Intelligence (AI) methods within industrial contexts, aiming to realize intelligent manufacturing, operation as well as maintenance, also known as industrial intelligence.
1 code implementation • 13 Oct 2023 • Mingjia Shi, Yuhao Zhou, Kai Wang, Huaizheng Zhang, Shudong Huang, Qing Ye, Jiangcheng Lv
Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data.
no code implementations • CVPR 2023 • Yuze Tan, Yixi Liu, Shudong Huang, Wentao Feng, Jiancheng Lv
Multi-view clustering have hitherto been studied due to their effectiveness in dealing with heterogeneous data.
no code implementations • 1 Nov 2022 • Hongyang He, Feng Ziliang, Yuanhang Zheng, Shudong Huang, HaoBing Gao
In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant technique. Encoder-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions properly. Recent research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range dependencies. By constructing and processing picture patches as embeddings, transformers have been applied to computer vision applications.
no code implementations • 27 Oct 2022 • Shudong Huang, Wentao Feng, Chenwei Tang, Jiancheng Lv
Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling.
no code implementations • 13 Oct 2022 • Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, Zhifeng Hao, Philip S. Yu, Lifang He
The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.
1 code implementation • 16 Sep 2019 • Zhao Kang, Guoxin Shi, Shudong Huang, Wenyu Chen, Xiaorong Pu, Joey Tianyi Zhou, Zenglin Xu
Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately.
1 code implementation • 13 Sep 2019 • Zhao Kang, Zipeng Guo, Shudong Huang, Siying Wang, Wenyu Chen, Yuanzhang Su, Zenglin Xu
Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie.