New Physics-Based Model Sheds Light on How Deep Neural Networks Learn Features
A Physical Review Letters study likens deep neural network feature learning to spring-block mechanics, linking data simplification to spring extension and nonlinearity to friction. The model reveals how noise can balance separation across layers and help predict performance, offering a powerful tool to optimise training, improve generalisation, and enhance efficiency in large AI systems.
