Interpretable Network

Interpretable networks aim to overcome the "black box" nature of deep learning models by making their internal decision-making processes transparent and understandable. Current research focuses on developing architectures and training methods that promote modularity, exploit feature dependencies, and leverage techniques like generative models and Bayesian approaches to enhance interpretability while maintaining predictive accuracy. This pursuit is significant because it addresses crucial concerns about trust and reliability in AI systems, paving the way for wider adoption in sensitive applications like healthcare and finance where understanding model decisions is paramount.