Occam Algorithm

Occam's Razor, the principle favoring simpler explanations, is being actively investigated in the context of machine learning, particularly deep neural networks. Current research focuses on leveraging this principle to improve model efficiency, reduce overfitting, and enhance interpretability, exploring techniques like network sparsification, Bayesian methods, and architectural modifications that inherently promote simpler solutions. These efforts aim to address challenges such as computational cost, dataset bias, and the "black box" nature of many deep learning models, ultimately leading to more robust, efficient, and understandable AI systems.