On the Expressive Power of Transformers for Maxout Networks and Continuous Piecewise Linear Functions

Researchers establish theoretical foundations for Transformer networks' expressive power by connecting them to maxout networks and continuous piecewise linear functions. The study proves Transformers inherit universal approximation capabilities of ReLU networks while revealing that self-attention layers implement max-type operations and feedforward layers perform token-wise affine transformations.