deep-manifold

This article delves into transfer learning from the perspective of category theory, proposing deep manifold theory. It argues that neural networks learn relational structures through attribute-free numerical computation, thereby enabling cross-domain transfer, and explains the internal logic of classification.

This article presents a mathematical perspective on why modern neural networks accommodate diverse architectures and attention mechanisms, framing them as different implementations of constraints within a learnable numerical system.