3 papers
cs.CL2026
Advancing Expert Specialization for Better MoE
Hongcan Guo, Haolang Lu, Guoshun Nan +8
Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly use…
cs.CV2025
Zero-Training Task-Specific Model Synthesis for Few-Shot Medical Image Classification
Yao Qin, Yangyang Yan, YuanChao Yang +4
Deep learning models have achieved remarkable success in medical image analysis but are fundamentally constrained by the requirement for large-scale, meticulously annotated dataset…
cs.LG2025
Two Is Better Than One: Rotations Scale LoRAs
Hongcan Guo, Guoshun Nan, Yuan Yang +9
Scaling Low-Rank Adaptation (LoRA)-based Mixture-of-Experts (MoE) facilitates large language models (LLMs) to efficiently adapt to diverse tasks. However, traditional gating mechan…