Once we assume that approximation, we also assume that for an input consisting of NxM numbers, we could find a NxM matrix that (when multiplied elementwise) could approximate the predictions of the MLP-Mixer for a class.



We will call this matrix a stencil, and if we have access to it, we can find what parts of the input embeddings are responsible for a specific prediction.
You can think of it as a punch card with holes in specific positions. Only information in those positions will pass and contribute to a specific prediction. So we can measure the intensity of the input at those positions.


