Divergent Ensemble Networks : Improving Predictive Reliability and Computational Efficiency

The effectiveness of ensemble learning in improving prediction accuracy and estimating uncertainty is wellestablished. However, conventional ensemble methods often grapple with high computational demands and redundant parameters due to independent network training. This study introduces the Divergent Ensemble Network (DEN), a novel framework designed to optimize computational efficiency while maintaining prediction diversity. DEN achieves superior predictive reliability with reduced parameter overhead by leveraging shared representation learning and independent branching. Our results demonstrate the efficacy of DEN in balancing accuracy, uncertainty estimation, and scalability, making it a robust choice for realworld applications.