11 citations · 16 across the 4 of their papers we have counts for
4 papers
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
Xiaojun Guo, Yifei Wang, Zeming Wei +1
With the prosperity of contrastive learning for visual representation learning (VCL), it is also adapted to the graph domain and yields promising performance. However, through a sy…
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond
Xiaojun Guo, Yifei Wang, Tianqi Du +1
Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance worsens as the number of layers increases. Instead of chara…
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
Yifei Wang, Qi Zhang, Tianqi Du +3
In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning…
Optimization-Induced Graph Implicit Nonlinear Diffusion
Qi Chen, Yifei Wang, Yisen Wang +2
Due to the over-smoothing issue, most existing graph neural networks can only capture limited dependencies with their inherently finite aggregation layers. To overcome this limitat…