Mixture of Experts (MoE) {NLP}
Description
Mixture of Experts (MoE) is an advanced neural network architecture that increases model capacity and efficiency by routing inputs through a subset of specialized "expert" sub-networks. Unlike traditional dense models, MoE dynamically selects which experts to activate for each input, allowing for sparse computation and scalable training.
- Scalable and Efficient: Enables training of extremely large models by activating only a fraction of the network per input, reducing computational cost while maintaining high capacity.
- Dynamic Routing: Utilizes a gating mechanism to select the most relevant experts for each input, promoting specialization and diversity among experts.
- State-of-the-Art Performance: Achieves strong results in language modeling and other tasks, especially in large-scale settings. MoE models have powered some of the largest and most capable language models to date.
- Flexible Design: Can be integrated into various architectures, including Transformers, to enhance performance on tasks involving diverse or complex data distributions.