419 citations · 932 across the 46 of their papers we have counts for
6 papers · 1 filter
On Self-Distilling Graph Neural Network
Yuzhao Chen, Yatao Bian, Xi Xiao +3
Recently, the teacher-student knowledge distillation framework has demonstrated its potential in training Graph Neural Networks (GNNs). However, due to the difficulty of training o…
Graph Information Bottleneck for Subgraph Recognition
Junchi Yu, Tingyang Xu, Yu Rong +3
Given the input graph and its label/property, several key problems of graph learning, such as finding interpretable subgraphs, graph denoising and graph compression, can be attribu…
Self-Supervised Graph Transformer on Large-Scale Molecular Data
Yu Rong, Yatao Bian, Tingyang Xu +4
How to obtain informative representations of molecules is a crucial prerequisite in AI-driven drug design and discovery. Recent researches abstract molecules as graphs and employ G…
Continuous Submodular Function Maximization
Yatao Bian, Joachim M. Buhmann, Andreas Krause
Continuous submodular functions are a category of generally non-convex/non-concave functions with a wide spectrum of applications. The celebrated property of this class of function…
Multi-View Graph Neural Networks for Molecular Property Prediction
Hehuan Ma, Yatao Bian, Yu Rong +5
The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through Graph…
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Aytunc Sahin, Yatao Bian, Joachim M. Buhmann +1
Submodular functions have been studied extensively in machine learning and data mining. In particular, the optimization of submodular functions over the integer lattice (integer su…