4 citations · 11 across the 17 of their papers we have counts for
9 papers · 1 filter
Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement
Siyuan Du, Mengxi Chen, Xinyang Jiang +6
Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex over…
Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach
Haolin Li, Shuyang Jiang, Ruipeng Zhang +3
While large language models hold promise for complex medical applications, their development is hindered by the scarcity of high-quality reasoning data. To address this issue, exis…
Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Data
Feng Hong, Yu Huang, Zihua Zhao +5
Real-world datasets for deep learning frequently suffer from the co-occurring challenges of class imbalance and label noise, hindering model performance. While methods exist for ea…
RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis
Haolin Li, Tianjie Dai, Zhe Chen +4
Clinical diagnosis is a highly specialized discipline requiring both domain expertise and strict adherence to rigorous guidelines. While current AI-driven medical research predomin…
Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection
Ziqing Fan, Siyuan Du, Shengchao Hu +5
Selecting high-quality pre-training data for large language models (LLMs) is crucial for enhancing their overall performance under limited computation budget, improving both traini…
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
Ziqing Fan, Shengchao Hu, Yuhang Zhou +4
The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy applicable to diverse tasks without the need for online environmental interaction. Re…
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
Ziqing Fan, Shengchao Hu, Jiangchao Yao +4
In federated learning (FL), the multi-step update and data heterogeneity among clients often lead to a loss landscape with sharper minima, degenerating the performance of the resul…
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
Shengchao Hu, Ziqing Fan, Li Shen +3
The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy applicable to diverse tasks without the need for online environmental interaction. Re…
Q-value Regularized Transformer for Offline Reinforcement Learning
Shengchao Hu, Ziqing Fan, Chaoqin Huang +4
Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action distribution…