25 citations · 85 across the 12 of their papers we have counts for
12 papers
GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis
Shaoting Tan, Ning Liu, Yuntao Du +7
Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering. Existing Large Language Model (LLM) approaches exhibit a…
Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
Jiajun Cui, Minghe Yu, Bo Jiang +3
Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their futur…
Learning Interpretable Rules for Scalable Data Representation and Classification
Zhuo Wang, Wei Zhang, Ning Liu +1
Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. Howe…
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
Cong Xu, Jun Wang, Jianyong Wang +1
Embedding plays a key role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision-making models. I…
Knowledge-aware Collaborative Filtering with Pre-trained Language Model for Personalized Review-based Rating Prediction
Quanxiu Wang, Xinlei Cao, Jianyong Wang +1
Personalized review-based rating prediction aims at leveraging existing reviews to model user interests and item characteristics for rating prediction. Most of the existing studies…
Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today
Zhuo Wang, Rongzhen Li, Bowen Dong +8
Recent investigations show that large language models (LLMs), specifically GPT-4, not only have remarkable capabilities in common Natural Language Processing (NLP) tasks but also e…
Effective Few-Shot Named Entity Linking by Meta-Learning
Xiuxing Li, Zhenyu Li, Zhengyan Zhang +5
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g.,…
Scalable Rule-Based Representation Learning for Interpretable Classification
Zhuo Wang, Wei Zhang, Ning Liu +1
Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. Howe…
Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation
Zeyuan Chen, Wei Zhang, Junchi Yan +2
Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynam…
Graph-Based Tri-Attention Network for Answer Ranking in CQA
Wei Zhang, Zeyuan Chen, Chao Dong +3
In community-based question answering (CQA) platforms, automatic answer ranking for a given question is critical for finding potentially popular answers in early times. The mainstr…
IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning
Wei Zhang, Yunfeng Zhang, Ning Liu +2
This paper studies how to improve the generalization performance and learning speed of the navigation agents trained with deep reinforcement learning (DRL). Although DRL exhibits h…
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization
Zhuo Wang, Wei Zhang, Ning Liu +1
Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance,…