Entropy Estimation

Entropy estimation, the process of quantifying uncertainty or randomness in data, is crucial across diverse fields, from cryptography to machine learning. Current research focuses on improving the accuracy and efficiency of entropy estimation, particularly in high-dimensional spaces and for complex data types, employing techniques like kernel-based methods, variational inference, and machine learning models (including convolutional neural networks, recurrent neural networks, and transformers). These advancements are vital for enhancing the security of cryptographic systems, improving the performance of machine learning algorithms, and enabling more robust analysis of complex datasets in various scientific domains.