5 papers
Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability
Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4
Pre-trained language models such as BERT achieve strong text classification performance but lack transparency, limiting their use in high-stakes settings. The Tsetlin Machine (TM)…
FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
Junkang Liu, Fanhua Shang, Yuanyuan Liu +3
Although Federated Learning has been widely studied in recent years, there are still high overhead expenses in each communication round for large-scale models such as Vision Transf…
S2D-ALIGN: Shallow-to-Deep Auxiliary Learning for Anatomically-Grounded Radiology Report Generation
Jiechao Gao, Chang Liu, Yuangang Li
Radiology Report Generation (RRG) aims to automatically generate diagnostic reports from radiology images. To achieve this, existing methods have leveraged the powerful cross-modal…
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications
Jiechao Gao, Yuangang Li, Yue Zhao +1
The proliferation of Internet of Things (IoT) has increased interest in federated learning (FL) for privacy-preserving distributed data utilization. However, traditional two-tier F…
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems
Jiechao Gao, Yuangang Li
Personalized medication aims to tailor healthcare to individual patient characteristics. However, the heterogeneity of patient data across healthcare systems presents significant c…