Softmax Gating

Softmax gating is a crucial component of Mixture of Experts (MoE) models, which combine multiple specialized "expert" networks to improve the accuracy and efficiency of machine learning tasks. Current research focuses on improving the performance and sample efficiency of softmax gating, exploring alternative gating functions like sigmoid and investigating the impact of different architectures, such as hierarchical MoEs and dense-to-sparse gating, on model convergence and parameter estimation. These advancements aim to address limitations of softmax gating, such as representation collapse and slow convergence rates, leading to more robust and efficient large-scale models for applications ranging from image classification to recommendation systems.