1 citations · 3 across the 15 of their papers we have counts for
15 papers
KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
Xubin Chen, Yipeng Zhou, Wen Sun +3
Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for medical reimbursement, quality r…
From Classification to Recommendation: Empirical Analysis of Audio Embedding Models Application for Content-Based Music Recommendation
Qingrui Li, Haowei Lou, Chengkai Huang +2
Pretrained audio representation models learned from large-scale corpora have achieved strong performance in audio classification and understanding. However, most existing models ar…
Seeing and Reflecting: Multimodal Memory-Enhanced Agent Collaboration for Recommendation
Hao Cong, Huizu Lin, Zihan Wang +3
Large language model (LLM)-based agentic recommender systems show promise in modeling user preferences through natural-language reasoning, yet they remain limited by text-centric i…
Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs
Chengkai Huang, Tianqi Gao, Hongtao Huang +2
Semantic-ID-based generative recommendation has recently emerged as a scalable paradigm for sequential recommendation, where each item is represented by a compact sequence of discr…
Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG
Xihang Wang, Zihan Wang, Chengkai Huang +4
Multimodal Retrieval-Augmented Generation (MRAG) is widely adopted for Multimodal Large Language Models (MLLMs) with external evidence to reduce hallucinations. Despite its success…
MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG
Xihang Wang, Zihan Wang, Chengkai Huang +2
Multimodal Retrieval-Augmented Generation (MRAG) addresses key limitations of Multimodal Large Language Models (MLLMs), such as hallucination and outdated knowledge. However, curre…
Unlocking Prototype Potential: An Efficient Tuning Framework for Few-Shot Class-Incremental Learning
Shengqin Jiang, Xiaoran Feng, Yuankai Qi +6
Few-shot class-incremental learning (FSCIL) seeks to continuously learn new classes from very limited samples while preserving previously acquired knowledge. Traditional methods of…
Generative Chain of Behavior for User Trajectory Prediction
Chengkai Huang, Xiaodi Chen, Hongtao Huang +2
Modeling long-term user behavior trajectories is essential for understanding evolving preferences and enabling proactive recommendations. However, most sequential recommenders focu…
PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation
Shuguang Jiao, Xinyu Xiao, Yunfan Wei +4
Retrieval-augmented generation (RAG) has become a powerful framework for enhancing large language models in knowledge-intensive and reasoning tasks. However, as reasoning chains de…
Teaching Prompts to Coordinate: Hierarchical Layer-Grouped Prompt Tuning for Continual Learning
Shengqin Jiang, Tianqi Kong, Yuankai Qi +5
Prompt-based continual learning methods fine-tune only a small set of additional learnable parameters while keeping the pre-trained model's parameters frozen. It enables efficient…
Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registration
Xingmei Wang, Xiaoyu Hu, Chengkai Huang +4
Bridging 2D and 3D sensor modalities is critical for robust perception in autonomous systems. However, image-to-point cloud (I2P) registration remains challenging due to the semant…
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning
Lishan Yang, Wei Emma Zhang, Quan Z. Sheng +3
In the era of big data, data mining has become indispensable for uncovering hidden patterns and insights from vast and complex datasets. The integration of multimodal data sources…
Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising
Yao Liu, Quan Z. Sheng, Lina Yao
Pedestrian trajectory prediction plays a pivotal role in the realms of autonomous driving and smart cities. Despite extensive prior research employing sequence and generative model…
Multi-agent Traffic Prediction via Denoised Endpoint Distribution
Yao Liu, Ruoyu Wang, Yuanjiang Cao +2
The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering…
Unveiling Contextual Similarity of Things via Mining Human-Thing Interactions in the Internet of Things
Lina Yao, Quan Z. Sheng, Anne H. H. Ngu +2
With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous…