一作ICML 2026Poster· Deep Learning / Foundation Models
Rethinking Sparse Mixture of Experts from a Unified Perspective
Giang Do · Hung Le · Truyen Tran
效率与系统OpenReview
首次获取该候选人的完整档案及 StartLux 评估共 20 积分；每位用户单独解锁，之后查看免费。主动刷新 20 积分。
实时查找电子邮箱，费用不超过20积分；未找到邮箱不扣费。
Giang Do · Hung Le · Truyen Tran
Giang Do · Hung Le · Truyen Tran
Giang Do · Hung Le · Truyen Tran
In the era of Large Language Models (LLMs), the Mixture of Experts (MoE) architecture has emerged as an effective approach for training extremely large models with improved computational efficiency. This success builds upon extensive prior research aimed at enhancing expert specialization in MoE-...