Adaptive Mixture

Adaptive Mixture models, a class of machine learning architectures, aim to improve efficiency and performance by selectively activating specialized sub-models (experts) based on input characteristics. Current research focuses on developing adaptive gating mechanisms to dynamically select experts, often employing low-rank adaptation (LoRA) for efficient parameterization and leveraging pre-trained dense models to accelerate training. These techniques are being applied across diverse fields, including natural language processing, computer vision, and federated learning, demonstrating improvements in accuracy, resource efficiency, and fairness while addressing challenges like data heterogeneity and model drift.