2 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
Graph Pre-Training Models Are Strong Anomaly Detectors
Jiashun Cheng, Zinan Zheng, Yang Liu +5
Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results. The effectiveness of existing…
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
Zinan Zheng, Yang Liu, Jia Li +2
Incorporating Euclidean symmetries (e.g. rotation equivariance) as inductive biases into graph neural networks has improved their generalization ability and data efficiency in unbo…
SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification
Bin Cao, Yang Liu, Zinan Zheng +3
Spectroscopic data, particularly diffraction data, contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (X…
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
Haihong Zhao, Chenyi Zi, Yang Liu +3
Anomaly detection (AD) plays a pivotal role in numerous web-based applications, including malware detection, anti-money laundering, device failure detection, and network fault anal…