Investigating the Synergy of Variational Information Bottleneck and Iterative Transformer in a Hybrid Bottleneck...

This study explores the integration of the Variational Information Bottleneck (VIB) and the Perceiver model in a Hybrid Bottleneck (HB) framework for disease prediction, specifically focusing on the challenges of convergence speed and generalization in small, structured datasets. Leveraging VIB's information compression and the Perceiver's cross- and self-attention mechanisms, we hypothesize that this hybrid approach enhances model performance. We conducted ablation studies using three open datasets to assess the model's effectiveness across different disease prediction tasks. Our ablation study examines the impact of removing components such as the Transformer's auxiliary layers, residual connections, and VIB, evaluating their roles in model convergence and generalization. Results indicate that integrating VIB enhances predictive accuracy and stabilizes learning by filtering noise and retaining essential features, while the Transformer's attention mechanisms quicken convergence. The HB framework, particularly with the inclusion of Add & Norm components, demonstrates improved stability and robustness, particularly in noisy datasets. These findings underscore the synergistic potential of combining VIB and Perceiver components in biomedical predictive modeling, offering significant improvements in rapid convergence and training stability.