13 citations · 20 across the 4 of their papers we have counts for
4 papers
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
Xin Liu, Yuxiang Zhang, Meng Wu +6
Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e.…
A Novel Neural-symbolic System under Statistical Relational Learning
Dongran Yu, Xueyan Liu, Shirui Pan +2
A key objective in the field of artificial intelligence is to develop cognitive models that can exhibit human-like intellectual capabilities. One promising approach to achieving th…
Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
Guangsi Shi, Daokun Zhang, Ming Jin +2
The great learning ability of deep learning models facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeav…
A Survey on Neural-symbolic Learning Systems
Dongran Yu, Bo Yang, Dayou Liu +2
In recent years, neural systems have demonstrated highly effective learning ability and superior perception intelligence. However, they have been found to lack effective reasoning…