Shannon Entropy

Shannon entropy, a measure of uncertainty or randomness in a system, is a fundamental concept in information theory with applications across diverse scientific fields. Current research focuses on leveraging entropy to improve machine learning models, including enhancing model interpretability, optimizing training processes (e.g., through pruning and adaptive exploration-exploitation strategies), and improving robustness and generalization. This involves applying entropy-based metrics to analyze model behavior, guide data selection, and develop novel algorithms for tasks ranging from image classification to reinforcement learning, ultimately leading to more efficient and reliable AI systems.