62 citations · 124 across the 17 of their papers we have counts for
7 papers · 1 filter
Embedding Node Structural Role Identity Using Stress Majorization
Lili Wang, Chenghan Huang, Weicheng Ma +2
Nodes in networks may have one or more functions that determine their role in the system. As opposed to local proximity, which captures the local context of nodes, the role identit…
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…
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…
Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks
Weicheng Ma, Kai Zhang, Renze Lou +2
This paper studies the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks. Prior research has…
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
Lili Wang, Chongyang Gao, Chenghan Huang +3
Networks found in the real-world are numerous and varied. A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types. Accordingly,…
Mitigating Political Bias in Language Models Through Reinforced Calibration
Ruibo Liu, Chenyan Jia, Jason Wei +3
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world…