Definition
Mixture of Experts (MoE) is a machine learning architecture designed to improve efficiency and scalability. Instead of using a single large model for all tasks, MoE employs multiple smaller ’expert’ networks, each specialized in different aspects of the data. A trainable gating network determines which experts should handle specific inputs, allowing the model to activate only a subset of parameters for each token. This sparsity enables significantly larger model capacity with reduced computational cost during inference, making it ideal for large-scale language models.
Summary
An architectural pattern where multiple specialized neural networks (experts) are combined via a gating mechanism to process inputs.
Key Concepts
- Sparse Activation
- Gating Network
- Expert Specialization
- Scalability
Use Cases
- Training large language models efficiently
- Reducing inference latency for massive models
- Handling diverse input types in multimodal systems