613 citations · 858 across the 15 of their papers we have counts for
15 papers
Spatial Heterophily Aware Graph Neural Networks
Congxi Xiao, Jingbo Zhou, Jizhou Huang +2
Graph Neural Networks (GNNs) have been broadly applied in many urban applications upon formulating a city as an urban graph whose nodes are urban objects like regions or points of…
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach
Miao Chen, Xinjiang Lu, Tong Xu +4
Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables.…
AutoField: Automating Feature Selection in Deep Recommender Systems
Yejing Wang, Xiangyu Zhao, Tong Xu +1
Feature quality has an impactful effect on recommendation performance. Thereby, feature selection is a critical process in developing deep learning-based recommender systems. Most…
Adversarial Neural Trip Recommendation
Linlang Jiang, Jingbo Zhou, Tong Xu +4
Trip recommender system, which targets at recommending a trip consisting of several ordered Points of Interest (POIs), has long been treated as an important application for many lo…
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction
Shuangli Li, Jingbo Zhou, Tong Xu +2
Recently many efforts have been devoted to applying graph neural networks (GNNs) to molecular property prediction which is a fundamental task for computational drug and material di…
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning
Weijia Zhang, Hao Liu, Fan Wang +4
Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drive…
Drug Package Recommendation via Interaction-aware Graph Induction
Zhi Zheng, Chao Wang, Tong Xu +5
Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation.…
Out-of-Town Recommendation with Travel Intention Modeling
Haoran Xin, Xinjiang Lu, Tong Xu +4
Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-I…
Adam revisited: a weighted past gradients perspective
Hui Zhong, Zaiyi Chen, Chuan Qin +4
Adaptive learning rate methods have been successfully applied in many fields, especially in training deep neural networks. Recent results have shown that adaptive methods with expo…
Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction
Jingbo Zhou, Shuangli Li, Liang Huang +5
Accurately predicting the binding affinity between drugs and proteins is an essential step for computational drug discovery. Since graph neural networks (GNNs) have demonstrated re…
Disentangled Graph Collaborative Filtering
Xiang Wang, Hongye Jin, An Zhang +3
Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user…
Foreground-Background Imbalance Problem in Deep Object Detectors: A Review
Joya Chen, Qi Wu, Dong Liu +1
Recent years have witnessed the remarkable developments made by deep learning techniques for object detection, a fundamentally challenging problem of computer vision. Nevertheless,…
Integrating Graph Contextualized Knowledge into Pre-trained Language Models
Bin He, Di Zhou, Jinghui Xiao +4
Complex node interactions are common in knowledge graphs, and these interactions also contain rich knowledge information. However, traditional methods usually treat a triple as a t…
A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring
Dazhong Shen, Qi Zhang, Tong Xu +7
Identifying the arrival times of seismic P-phases plays a significant role in real-time seismic monitoring, which provides critical guidance for emergency response activities. Whil…
Residual Objectness for Imbalance Reduction
Joya Chen, Dong Liu, Bin Luo +3
For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds. While several sampling/reweighting schemes have been explored to allevia…