78 citations · 265 across the 23 of their papers we have counts for
5 papers · 1 filter
Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?
Junxiang Wang, Xuchao Zhang, Bo Zong +5
During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstrea…
A Convergent ADMM Framework for Efficient Neural Network Training
Junxiang Wang, Hongyi Li, Liang Zhao
As a well-known optimization framework, the Alternating Direction Method of Multipliers (ADMM) has achieved tremendous success in many classification and regression applications. R…
Community-based Layerwise Distributed Training of Graph Convolutional Networks
Hongyi Li, Junxiang Wang, Yongchao Wang +2
The Graph Convolutional Network (GCN) has been successfully applied to many graph-based applications. Training a large-scale GCN model, however, is still challenging: Due to the no…
Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework
Junxiang Wang, Hongyi Li, Zheng Chai +3
While Graph Neural Networks (GNNs) are popular in the deep learning community, they suffer from several challenges including over-smoothing, over-squashing, and gradient vanishing.…
Sign-regularized Multi-task Learning
Johnny Torres, Guangji Bai, Junxiang Wang +3
Multi-task learning is a framework that enforces different learning tasks to share their knowledge to improve their generalization performance. It is a hot and active domain that s…