mathematical-framework

This paper proposes a unified geometric framework for understanding concept learning and neuron interpretation in sparse autoencoders, formalizing concepts as sets and defining detection, separation, and approximation. It provides error bounds, capacity constraints, and links to formal concept analysis, with experiments on synthetic data.