Multiple Model

Multiple model approaches in machine learning aim to improve performance, robustness, and fairness by combining the strengths of several individual models. Current research focuses on efficient model merging techniques, such as layer-wise integration and canonical correlation analysis, as well as strategies for selecting and weighting models within ensembles, including dynamic model selection and multilingual arbitrage. This field is significant because it addresses limitations of single models, leading to improved accuracy, reduced bias, and more efficient resource utilization across diverse applications, from autonomous driving to medical diagnosis.