Combining Deep Learning

Combining deep learning models focuses on improving performance and efficiency by merging multiple networks trained on the same or different tasks. Current research explores diverse methods, including optimizing supernets for feature reconstruction, aligning models using canonical correlation analysis, and integrating deep learning with symbolic regression or traditional machine learning algorithms like GARCH models. These techniques find applications across various fields, from improving image registration in digital pathology and enhancing financial forecasting to optimizing resource allocation in complex problems like vehicle routing and enabling more efficient few-shot learning in natural language processing.