Year: 2025
Type: article
Abstract: This comprehensive article delves into the Mixture of Experts (MoE) architecture, a revolutionary approach to building scalable artificial intelligence systems. The article examines how MoE departs from traditional monolithic neural networks by employing multiple specialized experts and dynamic routing mechanisms. Through analysis of various implementations and applications, the article demonstrates MoE's effectiveness in achieving computational efficiency, handling diverse tasks, and maintaining performance while reducing resource requirements. The investigation covers the fundamental architecture, gating mechanisms, technical implementation challenges, and real-world applications across domains including language processing, computer vision, and medical imaging. The article also addresses critical aspects of training complexity, load balancing strategies, and future directions in automated architecture search and efficient training methods.
Author: Vasudev Daruvuri
Institution: University of Cincinnati
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407274604