MLP-Mixer Interpretability [D][R]

Hello everyone,
I am just looking for materials and papers to interpret the features learnt by MLP-Mixer models.
For ConvNets we can plot convolutional kernels and pattern that maximally activate units, so we have at least a vague idea of what the net captures and responds to.
For Vision Transformers, linear embedding is analogue to the first conv layer and we can still see things such as color blurs, edge detectors, color filters. For self-supervised pretrained models we sometimes see …

embedding features hello interpretability least linear machinelearning materials mlp plot transformers units vision vision transformers