271 citations · 347 across the 11 of their papers we have counts for
11 papers
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction
Xiaowei Mao, Haomin Wen, Hengrui Zhang +5
Pick-up and Delivery Route Prediction (PDRP), which aims to estimate the future service route of a worker given his current task pool, has received rising attention in recent years…
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
Qitian Wu, Wentao Zhao, Chenxiao Yang +5
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class o…
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis +7
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from w…
Localized Contrastive Learning on Graphs
Hengrui Zhang, Qitian Wu, Yu Wang +3
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its qu…
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
Yu Wang, Hengrui Zhang, Zhiwei Liu +2
Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems. However, current methods…
Heuristic Adaptability to Input Dynamics for SpMM on GPUs
Guohao Dai, Guyue Huang, Shang Yang +6
Sparse Matrix-Matrix Multiplication (SpMM) has served as fundamental components in various domains. Many previous studies exploit GPUs for SpMM acceleration because GPUs provide hi…
Handling Distribution Shifts on Graphs: An Invariance Perspective
Qitian Wu, Hengrui Zhang, Junchi Yan +1
There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight.…
Machine Learning for Electronic Design Automation: A Survey
Guyue Huang, Jingbo Hu, Yifan He +13
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in…
CogDL: A Comprehensive Library for Graph Deep Learning
Yukuo Cen, Zhenyu Hou, Yan Wang +15
Graph neural networks (GNNs) have attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications fr…
TP-TIO: A Robust Thermal-Inertial Odometry with Deep ThermalPoint
Shibo Zhao, Peng Wang, Hengrui Zhang +2
To achieve robust motion estimation in visually degraded environments, thermal odometry has been an attraction in the robotics community. However, most thermal odometry methods are…
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
Qitian Wu, Hengrui Zhang, Xiaofeng Gao +4
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assu…