Improving Deep Learning Performance with Mixture of Experts and Sparse Activation

The increasing complexity and scale of modern machine learning models have led to growing computational demands, raising concerns about efficiency, scalability, and adaptability. Traditional deep learning architectures often struggle to balance computational cost with model expressiveness, particularly in tasks requiring specialization across diverse data distributions. One promising solution is the use of modular architectures that allow selective activation of parameters, enabling efficient resource allocation while maintaining high performance. Mixture of Experts (MoE) is a widely adopted modular approach that partitions the model into multiple specialized experts, dynamically selecting a subset of them for each input. This technique has demonstrated remarkable success in large-scale machine learning applications, including natural language processing, computer vision, speech recognition, and recommendation systems. By leveraging sparse activation, MoE architectures achieve significant computational savings while scaling to billions of parameters. This survey provides a comprehensive overview of MoE, covering its fundamental principles, architectural variations, training strategies, and key applications. Additionally, we discuss the major challenges associated with MoE, including training stability, expert imbalance, interpretability, and hardware constraints. Finally, we explore potential future research directions aimed at improving efficiency, fairness, and real-world deployability. As machine learning continues to advance, MoE is poised to play a crucial role in the development of scalable and adaptive AI systems.