73 citations · 91 across the 6 of their papers we have counts for
7 papers
Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Network
Yiqiang Yi, Xu Wan, Kangfei Zhao +2
Binding affinity prediction of three-dimensional (3D) protein ligand complexes is critical for drug repositioning and virtual drug screening. Existing approaches transform a 3D pro…
Transformer for Graphs: An Overview from Architecture Perspective
Erxue Min, Runfa Chen, Yatao Bian +7
Recently, Transformer model, which has achieved great success in many artificial intelligence fields, has demonstrated its great potential in modeling graph-structured data. Till n…
Uncertainty-aware Cardinality Estimation by Neural Network Gaussian Process
Kangfei Zhao, Jeffrey Xu Yu, Zongyan He +1
Deep Learning (DL) has achieved great success in many real applications. Despite its success, there are some main problems when deploying advanced DL models in database systems, su…
Towards Expectation-Maximization by SQL in RDBMS
Kangfei Zhao, Jeffrey Xu Yu, Yu Rong +2
Integrating machine learning techniques into RDBMSs is an important task since there are many real applications that require modeling (e.g., business intelligence, strategic analys…
Dirichlet Graph Variational Autoencoder
Jia Li, Tomasyu Yu, Jiajin Li +5
Graph Neural Networks (GNNs) and Variational Autoencoders (VAEs) have been widely used in modeling and generating graphs with latent factors. However, there is no clear explanation…
Towards Feature-free TSP Solver Selection: A Deep Learning Approach
Kangfei Zhao, Shengcai Liu, Yu Rong +1
The Travelling Salesman Problem (TSP) is a classical NP-hard problem and has broad applications in many disciplines and industries. In a large scale location-based services system,…