116 citations · 286 across the 16 of their papers we have counts for
7 papers · 1 filter
Creating Training Sets via Weak Indirect Supervision
Jieyu Zhang, Bohan Wang, Xiangchen Song +4
Creating labeled training sets has become one of the major roadblocks in machine learning. To address this, recent \emph{Weak Supervision (WS)} frameworks synthesize training label…
Graph Pointer Neural Networks
Tianmeng Yang, Yujing Wang, Zhihan Yue +3
Graph Neural Networks (GNNs) have shown advantages in various graph-based applications. Most existing GNNs assume strong homophily of graph structure and apply permutation-invarian…
Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation
Yankai Chen, Yaming Yang, Yujing Wang +3
To alleviate data sparsity and cold-start problems of traditional recommender systems (RSs), incorporating knowledge graphs (KGs) to supplement auxiliary information has attracted…
Adaptive Transfer Learning on Graph Neural Networks
Xueting Han, Zhenhuan Huang, Bang An +1
Graph neural networks (GNNs) is widely used to learn a powerful representation of graph-structured data. Recent work demonstrates that transferring knowledge from self-supervised t…
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Defu Cao, Yujing Wang, Juanyong Duan +8
Multivariate time-series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-series temporal correlati…
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees
Jiangang Bai, Yujing Wang, Yiren Chen +4
Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. Meanwhile, syntactic information h…
Evolving Attention with Residual Convolutions
Yujing Wang, Yaming Yang, Jiangang Bai +6
Transformer is a ubiquitous model for natural language processing and has attracted wide attentions in computer vision. The attention maps are indispensable for a transformer model…