Non Uniform

Non-uniformity, encompassing uneven distributions of data, parameters, or actions, is a pervasive challenge across diverse scientific domains, driving research aimed at developing robust and efficient methods to handle such irregularities. Current efforts focus on adapting existing models, such as neural networks and gradient descent algorithms, to account for non-uniformity through techniques like non-uniform sampling, hyperparameter optimization, and novel cost functions. Addressing non-uniformity is crucial for improving the accuracy, efficiency, and generalizability of various applications, ranging from image processing and machine learning to environmental modeling and control systems.