Gated Mixture

Gated Mixture of Experts (MoE) models are a powerful neural network architecture designed to improve efficiency and performance by distributing computation across multiple specialized "expert" networks. Current research focuses on applying MoE to diverse tasks, including image rendering, video relationship detection, and speech recognition, often employing sparsely-gated mechanisms to control computational cost. This approach offers significant advantages in handling heterogeneous data and scaling model capacity without a proportional increase in computational complexity, leading to improved accuracy and efficiency in various applications.