271 citations · 596 across the 31 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…
DiffPuter: Empowering Diffusion Models for Missing Data Imputation
Hengrui Zhang, Liancheng Fang, Qitian Wu +1
Generative models play an important role in missing data imputation in that they aim to learn the joint distribution of full data. However, applying advanced deep generative models…
SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
Wujiang Xu, Qitian Wu, Zujie Liang +5
Sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions. The SR models examine the sequence of a user…
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…