3 papers
cs.AI2026
BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE
Juntong Wu, Jialiang Cheng, Qishen Yin +5
Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standard MoE employs a fixed Top-K…
cs.LG2026
SERE: Similarity-based Expert Re-routing for Efficient Batch Decoding in MoE Models
Juntong Wu, Jialiang Cheng, Fuyu Lv +2
Mixture-of-Experts (MoE) architectures employ sparse activation to deliver faster training and inference with higher accuracy than dense LLMs. However, in production serving, MoE m…
cs.CV2023
COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation
Jia-Qi Yang, Chenglei Dai, Dan OU +5
With the advancement of multimedia internet, the impact of visual characteristics on the decision of users to click or not within the online retail industry is increasingly signifi…