5 papers · 1 filter
Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage
Chenguang Wang, Xiaoyu Zhang, Kaiyuan Cui +3
Training neural samplers directly from unnormalized densities without access to target distribution samples presents a significant challenge. A critical desideratum in these settin…
FlexiDrop: Theoretical Insights and Practical Advances in Random Dropout Method on GNNs
Zhiheng Zhou, Sihao Liu, Weichen Zhao
Graph Neural Networks (GNNs) are powerful tools for handling graph-type data. Recently, GNNs have been widely applied in various domains, but they also face some issues, such as ov…
Graph Neural Aggregation-diffusion with Metastability
Kaiyuan Cui, Xinyan Wang, Zicheng Zhang +1
Continuous graph neural models based on differential equations have expanded the architecture of graph neural networks (GNNs). Due to the connection between graph diffusion and mes…
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
Weichen Zhao, Chenguang Wang, Xinyan Wang +3
This paper presents an analytical study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). Generalizing beyond extant approaches grounded in random walk an…
Improved Naive Bayes with Mislabeled Data
Qianhan Zeng, Yingqiu Zhu, Xuening Zhu +5
Labeling mistakes are frequently encountered in real-world applications. If not treated well, the labeling mistakes can deteriorate the classification performances of a model serio…