11 citations · 25 across the 18 of their papers we have counts for
6 papers · 1 filter
Pioneering Reliable Assessment in Text-to-Image Knowledge Editing: Leveraging a Fine-Grained Dataset and an Innovative Criterion
Hengrui Gu, Kaixiong Zhou, Yili Wang +2
During pre-training, the Text-to-Image (T2I) diffusion models encode factual knowledge into their parameters. These parameterized facts enable realistic image generation, but they…
Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models
Yili Wang, Kaixiong Zhou, Ninghao Liu +2
Sharpness-aware minimization (SAM) has received increasing attention in computer vision since it can effectively eliminate the sharp local minima from the training trajectory and m…
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
Yili Wang, Yixin Liu, Xu Shen +6
To build safe and reliable graph machine learning systems, unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection (GLOD)…
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
Rui Miao, Kaixiong Zhou, Yili Wang +3
Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node…
Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias
Xin He, Wenqi Fan, Ruobing Wang +4
Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplif…
Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models
Xu Shen, Yili Wang, Kaixiong Zhou +2
The open-world test dataset is often mixed with out-of-distribution (OOD) samples, where the deployed models will struggle to make accurate predictions. Traditional detection metho…