444 citations · 497 across the 49 of their papers we have counts for
13 papers · 1 filter
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
Botao Wang, Jia Li, Heng Chang +2
In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect anal…
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
Lingzheng Zhang, Lifeng Shen, Yimin Zheng +3
Recent research has shown that large language models (LLMs) can be effectively used for real-world time series forecasting due to their strong natural language understanding capabi…
Graph Pre-Training Models Are Strong Anomaly Detectors
Jiashun Cheng, Zinan Zheng, Yang Liu +5
Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results. The effectiveness of existing…
Toward Physics-guided Time Series Embedding
Jiaxi Hu, Bowen Zhang, Qingsong Wen +2
In various scientific and engineering fields, the primary research areas have revolved around physics-based dynamical systems modeling and data-driven time series analysis. Accordi…
MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification
Yin Li, Liangwei Wang, Shiyuan Piao +4
Large Language Models (LLMs) have demonstrated significant potential in code generation. However, in the factory automation sector, particularly motion control, manual programming,…
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
Ryotaro Shimizu, Takashi Wada, Yu Wang +9
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between t…
Agent-Oriented Planning in Multi-Agent Systems
Ao Li, Yuexiang Xie, Songze Li +3
Through the collaboration of multiple LLM-empowered agents possessing diverse expertise and tools, multi-agent systems achieve impressive progress in solving real-world problems. G…
Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast
Man Li, Ziyue Li, Lijun Sun +1
Tensor decomposition has emerged as a prominent technique to learn low-dimensional representation under the supervision of reconstruction error, primarily benefiting data inference…
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
Jiexia Ye, Weiqi Zhang, Ziyue Li +3
The recent rapid advancements in language models (LMs) have garnered attention in medical time series-text multimodal learning. However, existing contrastive learning-based and pro…
Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey
Jiexia Ye, Yongzi Yu, Weiqi Zhang +3
Time series data are ubiquitous across diverse real-world applications, making time series analysis critically important. Traditional approaches are largely task-specific, offering…
MultiFun-DAG: Multivariate Functional Directed Acyclic Graph
Tian Lan, Ziyue Li, Junpeng Lin +6
Directed Acyclic Graphical (DAG) models efficiently formulate causal relationships in complex systems. Traditional DAGs assume nodes to be scalar variables, characterizing complex…
Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling
Jiuyun Hu, Ziyue Li, Chen Zhang +2
Tensor clustering has become an important topic, specifically in spatio-temporal modeling, due to its ability to cluster spatial modes (e.g., stations or road segments) and tempora…
Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances
Yanrong Li, Juan Du, Fugee Tsung +1
With the development and popularity of sensors installed in manufacturing systems, complex data are collected during manufacturing processes, which brings challenges for traditiona…