From Approximation to Emergence: A Theory of Deep Learning

A new academic paper titled From Approximation to Emergence attempts to synthesize fragmented concepts in deep learning into a singular, proof-based theoretical framework. By linking classical mathematical foundations with the complex behaviors observed in large-scale models, the authors aim to provide a more rigorous explanation for how modern neural networks transition from basic data approximation to sophisticated emergent capabilities.