10 citations · 21 across the 10 of their papers we have counts for
12 papers
UniGFM-RAG: Unify Graph Foundation Models and Large Language Models for End-to-End Retrieval and Reasoning
Zicheng Zhao, Linhao Luo, Zongji Yu +4
Large Language Models (LLMs) have shown remarkable capabilities across tasks, but they still struggle with complex reasoning that requires integrating scattered knowledge. Graph Re…
Unlocking the Specialization Capability of PPG Foundation Models for Wearable Signals through Structured Pruning and Adaptive Fine-Tuning
Huaming Du, Zhixian Xie, Gaoyang Zhen +6
Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. However, existing approaches to universal PPG representation learning a…
EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning
Hengyu Zhang, Xuyun Zhang, Pengxiang Zhan +5
Recent advances in large language models (LLMs) have enabled promising progress in diagnosis prediction from electronic health records (EHRs). However, existing LLM-based approache…
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
Xinting Liao, Weiming Liu, Chaochao Chen +7
Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised…
Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
Yanchao Tan, Hang Lv, Pengxiang Zhan +2
Graphs with abundant attributes are essential in modeling interconnected entities and enhancing predictions across various real-world applications. Traditional Graph Neural Network…
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
Xinting Liao, Chaochao Chen, Weiming Liu +7
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective i…
Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness
Shide Du, Zihan Fang, Shiyang Lan +4
As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize t…
HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning
Xinting Liao, Weiming Liu, Chaochao Chen +5
Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among client…
Partial Relaxed Optimal Transport for Denoised Recommendation
Yanchao Tan, Carl Yang Member, Xiangyu Wei +2
The interaction data used by recommender systems (RSs) inevitably include noises resulting from mistaken or exploratory clicks, especially under implicit feedbacks. Without proper…
Multi-Modal Fusion in Contact-Rich Precise Tasks via Hierarchical Policy Learning
Piaopiao Jin, Yinjie Lin, Yanchao Tan +2
Combined visual and force feedback play an essential role in contact-rich robotic manipulation tasks. Current methods focus on developing the feedback control around a single modal…
Multi-Facet Recommender Networks with Spherical Optimization
Yanchao Tan, Carl Yang, Xiangyu Wei +2
Implicit feedback is widely explored by modern recommender systems. Since the feedback is often sparse and imbalanced, it poses great challenges to the learning of complex interact…
FinBrain: When Finance Meets AI 2.0
Xiaolin Zheng, Mengying Zhu, Qibing Li +2
Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation. As one of the new intelligent needs in the AI 2.0 era, financial inte…