9 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.DB2024
FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Network
Yi Zhan, Yang Sun, Han Weng +7
In this paper, we propose a novel graph-based methodology to evaluate the functional correctness of SQL generation. Conventional metrics for assessing SQL code generation, such as…
cs.LG2023★ 9 cited
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Weitao Du, Yuanqi Du, Limei Wang +5
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks (GNNs), a co…
cs.IR2023★ 1 cited
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation
Yongqiang Han, Likang Wu, Hao Wang +5
Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item. Although lots of r…