Toy Models of Superposition

This post introduces the concept of 'superposition' in ReLU networks, where models can represent more features than their dimensionality. It demonstrates this phenomenon using toy models with sparse input features, showing that superposition allows for compression beyond linear models but introduces interference that requires nonlinear filtering. The work explores the conditions under which superposition occurs and its implications for feature representation.