3 papers
cs.AI2026
Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering
Yongxue Shan, Meihan Wu, Cundi Fang +2
Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts. Existing multi-hop KGQA methods mainly rely on topic-centere…
cs.CV2025
EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients
Meihan Wu, Tao Chang, Cui Miao +5
Federated learning research has recently shifted from Convolutional Neural Networks (CNNs) to Vision Transformers (ViTs) due to their superior capacity. ViTs training demands highe…
cs.RO2025
FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation
Cui Miao, Tao Chang, Meihan Wu +4
Vision-language-action (VLA) models have significantly advanced robotic manipulation by enabling robots to interpret language instructions for task execution. However, training the…