62 citations · 119 across the 26 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2023★ 2 cited
Graph-Level Embedding for Time-Evolving Graphs
Lili Wang, Chenghan Huang, Weicheng Ma +2
Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior w…
cs.LG2021
Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization
Lili Wang, Chenghan Huang, Weicheng Ma +2
Recent years have seen a rise in the development of representational learning methods for graph data. Most of these methods, however, focus on node-level representation learning at…
cs.LG2021
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks
Weicheng Ma, Renze Lou, Kai Zhang +2
A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically. This paper presents GradTS, an automatic auxiliary task selection m…