18 citations · 18 across the 2 of their papers we have counts for
3 papers
cs.LG2025
HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference
Shuzhang Zhong, Yanfan Sun, Ling Liang +3
The Mixture of Experts (MoE) architecture has demonstrated significant advantages as it enables to increase the model capacity without a proportional increase in computation. Howev…
cs.CV2025
Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients
Li Lun, Kunyu Feng, Qinglong Ni +5
Spiking neural networks (SNNs) have shown their competence in handling spatial-temporal event-based data with low energy consumption. Similar to conventional artificial neural netw…
cs.LG2024★ 18 cited
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
Shuzhang Zhong, Ling Liang, Yuan Wang +3
Mixture-of-Experts (MoE) models are designed to enhance the efficiency of large language models (LLMs) without proportionally increasing the computational demands. However, their d…