9 citations · 18 across the 6 of their papers we have counts for
6 papers
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
Yuanchen Bei, Hao Xu, Sheng Zhou +5
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the adva…
Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation
Huixuan Chi, Hao Xu, Hao Fu +5
Modeling the evolution of user preference is essential in recommender systems. Recently, dynamic graph-based methods have been studied and achieved SOTA for recommendation, majorit…
Ensemble Multi-Relational Graph Neural Networks
Yuling Wang, Hao Xu, Yanhua Yu +4
It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the…
Semantic interpretation for convolutional neural networks: What makes a cat a cat?
Hao Xu, Yuntian Chen, Dongxiao Zhang
The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the "black box" model. Fundament…
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs
Berkin Akin, Suyog Gupta, Yun Long +6
On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high co…
Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks
Bojing Feng, Haonan Xu, Wenfang Xue +1
Credit rating is an analysis of the credit risks associated with a corporation, which reflects the level of the riskiness and reliability in investing, and plays a vital role in fi…