Random Function

Random functions are a core concept in machine learning, with research focusing on understanding their properties and leveraging them for improved model design and training. Current investigations explore how architectural choices in neural networks (like MLPs and Transformers), and the addition of noise, influence the complexity and generalization capabilities of these random functions. This research is crucial for advancing our understanding of deep learning, particularly in areas like solving complex combinatorial problems and density modeling, where the ability to generate and control random functions with specific properties is paramount. Furthermore, the study of random functions is revealing potential quantum-classical computational advantages in machine learning tasks.