154 citations · 287 across the 8 of their papers we have counts for
18 papers
Class-Balancing Diffusion Models
Yiming Qin, Huangjie Zheng, Jiangchao Yao +2
Diffusion-based models have shown the merits of generating high-quality visual data while preserving better diversity in recent studies. However, such observation is only justified…
Cooperative Learning for Noisy Supervision
Hao Wu, Jiangchao Yao, Ya Zhang +1
Learning with noisy labels has gained the enormous interest in the robust deep learning area. Recent studies have empirically disclosed that utilizing dual networks can enhance the…
Collaborative Label Correction via Entropy Thresholding
Hao Wu, Jiangchao Yao, Jiajie Wang +3
Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels. W…
Learning with Group Noise
Qizhou Wang, Jiangchao Yao, Chen Gong +4
Machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pair…
Sparse-Interest Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao +4
Recent methods in sequential recommendation focus on learning an overall embedding vector from a user's behavior sequence for the next-item recommendation. However, from empirical…
Learning on Attribute-Missing Graphs
Xu Chen, Siheng Chen, Jiangchao Yao +3
Graphs with complete node attributes have been widely explored recently. While in practice, there is a graph where attributes of only partial nodes could be available and those of…