64 citations · 76 across the 16 of their papers we have counts for
18 papers
Efficient Prompt Learning for Traffic Forecasting
Qianru Zhang, Xinyi Gao, Alexander Zhou +3
Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administration. Spatio-temporal graph neu…
ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructions
Xingwei He, Qianru Zhang, Pengfei Chen +4
Instruction-following is a critical capability of Large Language Models (LLMs). While existing works primarily focus on assessing how well LLMs adhere to user instructions, they of…
FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction
Qianru Zhang, Chenglei Yu, Haixin Wang +5
Time series prediction, a crucial task across various domains, faces significant challenges due to the inherent complexities of time series data, including non-stationarity, multi-…
AutoHFormer: Efficient Hierarchical Autoregressive Transformer for Time Series Prediction
Qianru Zhang, Honggang Wen, Ming Li +4
Time series forecasting requires architectures that simultaneously achieve three competing objectives: (1) strict temporal causality for reliable predictions, (2) sub-quadratic com…
EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code
Yuhao Qing, Boyu Zhu, Mingzhe Du +9
Existing code generation benchmarks primarily evaluate functional correctness, with limited focus on code efficiency and often restricted to a single language like Python. To addre…
M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation
Qianru Zhang, Liang Qu, Honggang Wen +4
Sequential recommendation systems aim to predict users' next preferences based on their interaction histories, but existing approaches face critical limitations in efficiency and m…
Efficient Traffic Prediction Through Spatio-Temporal Distillation
Qianru Zhang, Xinyi Gao, Haixin Wang +2
Graph neural networks (GNNs) have gained considerable attention in recent years for traffic flow prediction due to their ability to learn spatio-temporal pattern representations th…
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions
Xingwei He, Qianru Zhang, A-Long Jin +2
Large Vision-Language Models (LVLMs) have achieved remarkable progress on visual perception and linguistic interpretation. Despite their impressive capabilities across various task…
HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
Qianru Zhang, Xinyi Gao, Haixin Wang +3
Spatial-temporal graph representations play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime predictio…
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security
Qianru Zhang, Peng Yang, Junliang Yu +4
The widespread adoption of smartphones and Location-Based Social Networks has led to a massive influx of spatio-temporal data, creating unparalleled opportunities for enhancing Poi…
Billiards Sports Analytics: Datasets and Tasks
Qianru Zhang, Zheng Wang, Cheng Long +1
Nowadays, it becomes a common practice to capture some data of sports games with devices such as GPS sensors and cameras and then use the data to perform various analyses on sports…
A Survey of Generative Techniques for Spatial-Temporal Data Mining
Qianru Zhang, Haixin Wang, Cheng Long +8
This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. Wi…
Graph Augmentation for Recommendation
Qianru Zhang, Lianghao Xia, Xuheng Cai +3
Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, ev…
Improving Factual Error Correction by Learning to Inject Factual Errors
Xingwei He, Qianru Zhang, A-Long Jin +3
Factual error correction (FEC) aims to revise factual errors in false claims with minimal editing, making them faithful to the provided evidence. This task is crucial for alleviati…
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
Qianru Zhang, Chao Huang, Lianghao Xia +3
Spatial-temporal graph learning has emerged as a promising solution for modeling structured spatial-temporal data and learning region representations for various urban sensing task…
Automated Spatio-Temporal Graph Contrastive Learning
Qianru Zhang, Chao Huang, Lianghao Xia +3
Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlati…
On Inferring User Socioeconomic Status with Mobility Records
Zheng Wang, Mingrui Liu, Cheng Long +3
When users move in a physical space (e.g., an urban space), they would have some records called mobility records (e.g., trajectories) generated by devices such as mobile phones and…
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Qianru Zhang, Zheng Wang, Cheng Long +5
Detecting anomalous trajectories has become an important task in many location-based applications. While many approaches have been proposed for this task, they suffer from various…