6 citations · 9 across the 2 of their papers we have counts for
3 papers
cs.LG2023★ 3 cited
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
Yingheng Wang, Yair Schiff, Aaron Gokaslan +4
While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning. Here, we p…
cs.LG2022★ 6 cited
Going Deeper into Permutation-Sensitive Graph Neural Networks
Zhongyu Huang, Yingheng Wang, Chaozhuo Li +1
The invariance to permutations of the adjacency matrix, i.e., graph isomorphism, is an overarching requirement for Graph Neural Networks (GNNs). Conventionally, this prerequisite c…
cs.LG2020
Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction
Yingheng Wang, Yaosen Min, Xin Chen +1
Drug-drug interaction(DDI) prediction is an important task in the medical health machine learning community. This study presents a new method, multi-view graph contrastive represen…