Divergent Ensemble Networks

View the paper here: Paper

The research paper has been accepted for presentation at the 2nd International Conference on NLP, AI & Information Retrieval (NLAII 2024). It will also be published in the conference proceedings by the International Journal on Cybernetics & Informatics (IJCI).

Abstract

This work introduces a novel architecture called the Divergent Ensemble Network (DEN), which is designed to improve uncertainty estimation in neural networks. Traditional ensemble methods often face challenges like computational inefficiency and parameter redundancy. DEN addresses these by combining shared representation learning with independent branching, offering both computational efficiency and robust predictions. Key Features

• Shared Input Layer: Captures common features across ensemble members, reducing computational redundancy.
• Independent Branching Layers: Maintains diversity in predictions while improving variance.
• Performance Improvements: Enhanced computational efficiency and uncertainty estimation, suitable for real-time and critical applications.

DEN outperforms conventional methods in scenarios requiring high accuracy and reliable uncertainty predictions. The research demonstrates its potential in areas like real-time analytics and medical image analysis.

The GitHub repository contains the implementation code for further exploration.Github

This publication highlights a step forward in advancing research on artificial intelligence and uncertainty quantification.