Francesco D'Amico

Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks

Scaling laws in deep learning -- empirical power-law relationships linking model performance to resource growth -- have emerged as simple yet striking regularities across architectures, datasets, and tasks. These laws are particularly impactful in guiding the design of state-of-the-art models, since…