1 code implementation • 29 Nov 2024 • Kaican Li, Weiyan Xie, Yongxiang Huang, Didan Deng, Lanqing Hong, Zhenguo Li, Ricardo Silva, Nevin L. Zhang
Fine-tuning foundation models often compromises their robustness to distribution shifts.
1 code implementation • 23 Dec 2023 • Tong Li, Jiale Deng, Yanyan Shen, Luyu Qiu, Yongxiang Huang, Caleb Chen Cao
Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs.
no code implementations • 13 Jul 2023 • Nevin L. Zhang, Kaican Li, Han Gao, Weiyan Xie, Zhi Lin, Zhenguo Li, Luning Wang, Yongxiang Huang
Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s).
no code implementations • 20 May 2023 • Jindi Zhang, Luning Wang, Dan Su, Yongxiang Huang, Caleb Chen Cao, Lei Chen
Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem.
1 code implementation • 13 May 2023 • Han Gao, Kaican Li, Weiyan Xie, Zhi Lin, Yongxiang Huang, Luning Wang, Caleb Chen Cao, Nevin L. Zhang
In this paper, we consider a third, lesser-known setting where a training domain is endowed with a collection of pairs of examples that share the same semantic information.
no code implementations • 6 Sep 2020 • Yongxiang Huang, Albert C. S. Chung
There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis.
no code implementations • 16 Sep 2019 • Yongxiang Huang, Albert C. S. Chung
Despite deep convolutional neural networks boost the performance of image classification and segmentation in digital pathology analysis, they are usually weak in interpretability for clinical applications or require heavy annotations to achieve object localization.
no code implementations • 27 Jul 2018 • Yongxiang Huang, Albert Chi-shing Chung
Although convolutional neural networks (CNN) have advantages in extracting discriminative features in image classification, directly training a CNN on high resolution histology images is computationally infeasible currently.