新研究分析MoE模型校准和不连续性 · 追踪4个来源

报道来源 [4]

1. arXiv cs.AI TIER_1 English(EN) · Gina Wong, Drew Prinster, Suchi Saria, Rama Chellappa, Anqi Liu · 2026-06-19 04:00

   面向分布偏移下的校准混合专家模型

   arXiv:2606.20544v1 Announce Type: new Abstract: Calibration aligns a model's predictive uncertainty with the frequencies of its empirical outcomes and is important for understanding and trusting reported probabilities. Recent work shows that enforcing calibration at the level of …

2. arXiv cs.AI TIER_1 English(EN) · Anqi Liu · 2026-06-18 17:55

   走向分布偏移下的校准混合专家模型

   Calibration aligns a model's predictive uncertainty with the frequencies of its empirical outcomes and is important for understanding and trusting reported probabilities. Recent work shows that enforcing calibration at the level of individual predictors can improve ensemble accur…

3. arXiv cs.LG TIER_1 English(EN) · Tho Tran Huu, Huu-Tuan Nguyen, Thien-Hai Nguyen, Nhat-Tri Ho, Viet-Hoang Tran, Tho Quan, Tan Minh Nguyen · 2026-06-18 04:00

   稀疏混合专家模型中不连续性的几何与随机分析

   arXiv:2606.19036v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (SMoE) architectures are now widely deployed in state-of-the-art language and vision models, where conditional routing allows scaling to very large networks. However, this very Top-$k$ expert selection that…

4. arXiv cs.LG TIER_1 English(EN) · Tan Minh Nguyen · 2026-06-17 13:06

   稀疏混合专家模型中不连续性的几何与随机分析

   Sparse Mixture-of-Experts (SMoE) architectures are now widely deployed in state-of-the-art language and vision models, where conditional routing allows scaling to very large networks. However, this very Top-$k$ expert selection that enables conditional routing also renders the SM…