32 citations · 39 across the 8 of their papers we have counts for
7 papers · 1 filter
SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity
Qitian Wu, Kai Yang, Hengrui Zhang +2
Learning representations on large graphs is a long-standing challenge due to the inter-dependence nature. Transformers recently have shown promising performance on small graphs tha…
Transformers from Diffusion: A Unified Framework for Neural Message Passing
Qitian Wu, David Wipf, Junchi Yan
Learning representations for structured data with certain geometries (e.g., observed or unobserved) is a fundamental challenge, wherein message passing neural networks (MPNNs) have…
GeoMix: Towards Geometry-Aware Data Augmentation
Wentao Zhao, Qitian Wu, Chenxiao Yang +1
Mixup has shown considerable success in mitigating the challenges posed by limited labeled data in image classification. By synthesizing samples through the interpolation of featur…
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang +1
Real-world data generation often involves certain geometries (e.g., graphs) that induce instance-level interdependence. This characteristic makes the generalization of learning mod…
Graph Out-of-Distribution Generalization via Causal Intervention
Qitian Wu, Fan Nie, Chenxiao Yang +2
Out-of-distribution (OOD) generalization has gained increasing attentions for learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation with distrib…
Supercharging Graph Transformers with Advective Diffusion
Qitian Wu, Chenxiao Yang, Kaipeng Zeng +1
The capability of generalization is a cornerstone for the success of modern learning systems. For non-Euclidean data, e.g., graphs, that particularly involves topological structure…
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf +2
A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considera…