3 papers
cs.LG2026
DynaMo: Runtime Switchable Quantization for MoE with Cross-Dataset Adaptation
Zihao Zheng, Xiuping Cui, Size Zheng +4
As the Mix-of-Experts (MoE) architecture increases the number of parameters in large models, there is an even greater need for model quantization. However, existing quantization me…
cs.CV2025
EaqVLA: Encoding-aligned Quantization for Vision-Language-Action Models
Feng Jiang, Zihao Zheng, Xiuping Cui +3
With the development of Embodied Artificial intelligence, the end-to-end control policy such as Vision-Language-Action (VLA) model has become the mainstream. Existing VLA models fa…
cs.LG2025
FedHQ: Hybrid Runtime Quantization for Federated Learning
Zihao Zheng, Ziyao Wang, Xiuping Cui +6
Federated Learning (FL) is a decentralized model training approach that preserves data privacy but struggles with low efficiency. Quantization, a powerful training optimization tec…