16 citations · 38 across the 19 of their papers we have counts for
6 papers · 1 filter
Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction
Yunxiao Shi, Wujiang Xu, Haimin Zhang +2
Modeling high-order feature interactions is crucial for click-through rate (CTR) prediction, yet traditional approaches typically predefine a maximum interaction order and exhausti…
Differential Encoding for Improved Representation Learning over Graphs
Haimin Zhang, Jiahao Xia, Min Xu
Combining the message-passing paradigm with the global attention mechanism has emerged as an effective framework for learning over graphs. The message-passing paradigm and the glob…
Conditional Local Feature Encoding for Graph Neural Networks
Yongze Wang, Haimin Zhang, Qiang Wu +1
Graph neural networks (GNNs) have shown great success in learning from graph-based data. The key mechanism of current GNNs is message passing, where a node's feature is updated bas…
Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs
Haimin Zhang, Min Xu
Message passing has become the dominant framework in graph representation learning. The essential idea of the message-passing framework is to update node embeddings based on the in…
RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks
Haimin Zhang, Min Xu
Studies continually find that message-passing graph convolutional networks suffer from the over-smoothing issue. Basically, the issue of over-smoothing refers to the phenomenon tha…
SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks
Haimin Zhang, Min Xu, Guoqiang Zhang +1
Graph convolutional networks have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborh…