Neural Tree

Neural trees combine the strengths of neural networks and decision trees, aiming to improve model interpretability while maintaining or exceeding the performance of traditional deep learning models. Current research focuses on developing novel neural tree architectures, such as those incorporating episodic memory, oblique splits, and modular structures, for applications ranging from autonomous driving and image classification to survival analysis and knowledge graph question answering. This hybrid approach addresses the "black box" nature of many deep learning models, offering increased transparency and facilitating better understanding of model decision-making processes in various domains.