116 citations · 286 across the 16 of their papers we have counts for
16 papers
Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs
Haozhen Zhang, Xueting Han, Xi Xiao +1
Temporal Graph Learning, which aims to model the time-evolving nature of graphs, has gained increasing attention and achieved remarkable performance recently. However, in reality,…
An Automated Question-Answering Framework Based on Evolution Algorithm
Sinan Tan, Hui Xue, Qiyu Ren +2
Building a deep learning model for a Question-Answering (QA) task requires a lot of human effort, it may need several months to carefully tune various model architectures and find…
Learning Multi-granularity User Intent Unit for Session-based Recommendation
Jiayan Guo, Yaming Yang, Xiangchen Song +4
Session-based recommendation aims to predict a user's next action based on previous actions in the current session. The major challenge is to capture authentic and complete user pr…
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…
AutoADR: Automatic Model Design for Ad Relevance
Yiren Chen, Yaming Yang, Hong Sun +7
Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, th…
Multivariate Time-series Anomaly Detection via Graph Attention Network
Hang Zhao, Yujing Wang, Juanyong Duan +7
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress…
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
Yihuan Mao, Yujing Wang, Chufan Wu +6
BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks. However, a…
Improving BERT with Self-Supervised Attention
Yiren Chen, Xiaoyu Kou, Jiangang Bai +1
One of the most popular paradigms of applying large pre-trained NLP models such as BERT is to fine-tune it on a smaller dataset. However, one challenge remains as the fine-tuned mo…
TextNAS: A Neural Architecture Search Space tailored for Text Representation
Yujing Wang, Yaming Yang, Yiren Chen +7
Learning text representation is crucial for text classification and other language related tasks. There are a diverse set of text representation networks in the literature, and how…
Customized Graph Embedding: Tailoring Embedding Vectors to different Applications
Bitan Hou, Yujing Wang, Ming Zeng +4
Graph is a natural representation of data for a variety of real-word applications, such as knowledge graph mining, social network analysis and biological network comparison. For th…