27 citations · 76 across the 14 of their papers we have counts for
15 papers
From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents
Yiqi Wang, Jiaqi Zhang, Zhangkai Wu +9
Large language model (LLM)-based agents are moving beyond text generation to planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration.…
Can LLMs Serve As Time Series Anomaly Detectors?
Manqing Dong, Hao Huang, Longbing Cao
An emerging topic in large language models (LLMs) is their application to time series forecasting, characterizing mainstream and patternable characteristics of time series. A relev…
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services
Manqing Dong, Zhanxiang Zhao, Yitong Geng +3
Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance. Existi…
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data
Zhipeng Luo, Zhixing He, Jin Wang +4
Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giv…
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction
Manqing Dong, Chunguang Pan, Zhipeng Luo
Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works tr…
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
Manqing Dong, Feng Yuan, Lina Yao +2
A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handl…
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
Manqing Dong, Feng Yuan, Lina Yao +3
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation r…
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
Xiang Zhang, Lina Yao, Manqing Dong +3
Objective: Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the world's population. Most of t…
Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning
Xiang Zhang, Xiaocong Chen, Lina Yao +2
Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e.g., computer version). However, a severe challenge faced by deep learning is the hi…
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals
Xiang Zhang, Xiaocong Chen, Manqing Dong +3
Synthesizing geometrical shapes from human brain activities is an interesting and meaningful but very challenging topic. Recently, the advancements of deep generative models like G…
Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships
Chaoran Huang, Lina Yao, Xianzhi Wang +3
Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Co…
A Survey on Expert Recommendation in Community Question Answering
Xianzhi Wang, Chaoran Huang, Lina Yao +2
Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most…
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
Manqing Dong, Lina Yao, Xianzhi Wang +2
Random forest and deep neural network are two schools of effective classification methods in machine learning. While the random forest is robust irrespective of the data domain, th…
Opinion Fraud Detection via Neural Autoencoder Decision Forest
Manqing Dong, Lina Yao, Xianzhi Wang +3
Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experienc…
NeuRec: On Nonlinear Transformation for Personalized Ranking
Shuai Zhang, Lina Yao, Aixin Sun +3
Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relat…