Mixture of Experts

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

Related Terms

• Sparse Transformers
• Conditional Computation
• Large Language Models
• Neural Architecture Search