4 citations · 7 across the 17 of their papers we have counts for
3 papers · 1 filter
Large Generative Graph Models
Yu Wang, Ryan A. Rossi, Namyong Park +6
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse f…
Forward Learning of Graph Neural Networks
Namyong Park, Xing Wang, Antoine Simoulin +5
Graph neural networks (GNNs) have achieved remarkable success across a wide range of applications, such as recommendation, drug discovery, and question answering. Behind the succes…
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
Puja Trivedi, Mark Heimann, Rushil Anirudh +2
While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains…