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Mixture of Experts (MoE) is a machine learning technique that uses multiple expert models to make predictions. Each expert specializes in different aspects of the data, and a gating network determines which expert to use for a given input. This approach can improve model performance and efficiency.
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![]() Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining Costin-Andrei Oncescu Qingyang Wu Wai Tong Chung Robert Wu Bryan Gopal Junxiong Wang Tri Dao Ben Athiwaratkun | |||
![]() CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing Yifan Zhou Tianshi Xu Jue Hong Ye Wu Meng Li | |||
![]() DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection Guoxin Ma Xiaoming Liu Zhanhan Zhang Chengzhengxu Li Shengchao Liu Yu Lan | |||
![]() Mixture-of-Transformers Learn Faster: A Theoretical Study on Classification Problems Hongbo Li Qinhang Wu Sen Lin Yingbin Liang Ness B. Shroff | |||
![]() MoME: Mixture of Visual Language Medical Experts for Medical Imaging Segmentation Arghavan Rezvani Xiangyi Yan Anthony T. Wu Kun Han Pooya Khosravi Xiaohui Xie | |||
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