34 citations · 41 across the 19 of their papers we have counts for
21 papers
Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting
Xingsheng Chen, Deyu Yi, Siu-Ming Yiu
Existing patching and multi-scale methods advance multivariate time series forecasting but treat learned representations as transient byproducts of prediction, lacking explicit mec…
SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting
Xingsheng Chen, Siu-Ming Yiu
Multivariate time series forecasting requires capturing the continuously evolving correlation structure among interacting variables. Existing state-space models process time series…
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…
A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era
Zongru Li, Xingsheng Chen, Honggang Wen +8
Molecular property prediction integrates quantum chemistry, cheminformatics, and deep learning to connect molecular structure with physicochemical and biological behavior. This sur…
UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
Xingsheng Chen, Xianpei Mu, Deyu Yi +6
Multivariate time series forecasting is fundamental to numerous domains such as energy, finance, and environmental monitoring, where complex temporal dependencies and cross-variabl…
MODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEs
Xingsheng Chen, Regina Zhang, Bo Gao +5
Time series prediction plays a pivotal role across diverse domains such as finance, healthcare, energy systems, and environmental modeling. However, existing approaches often strug…
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…
A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
Jiale Guo, Suizhi Huang, Mei Li +8
The integration of Large Language Models (LLMs) into software engineering has driven a transition from traditional rule-based systems to autonomous agentic systems capable of solvi…
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…
HMamba: Hyperbolic Mamba for Sequential Recommendation
Qianru Zhang, Honggang Wen, Wei Yuan +4
Sequential recommendation systems have become a cornerstone of personalized services, adept at modeling the temporal evolution of user preferences by capturing dynamic interaction…
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…
Time Series Analysis in Frequency Domain: A Survey of Open Challenges, Opportunities and Benchmarks
Qianru Zhang, Yuting Sun, Honggang Wen +6
Frequency-domain analysis has emerged as a powerful paradigm for time series analysis, offering unique advantages over traditional time-domain approaches while introducing new theo…
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…
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
Xingwei He, Zhenghao Lin, Yeyun Gong +7
Many natural language processing (NLP) tasks rely on labeled data to train machine learning models with high performance. However, data annotation is time-consuming and expensive,…