SVM for "after kernels" which are the CK and NTK matrices of trained NNs.

We observe that for the self-attention layers, the correlation of weights for the same head is stronger. Additionally, the best grouping might depend on the type of the layer (e.g., key, query, value, or To simplify the implementation, we treat all the different kernels in the self-attention as a type of fully-connected layer. We down-sample along each dimension to make the computation feasible. To relate with the Frobenius norm, we compute the square of each element and normalize the value. In Figure 5, we show the approximation error comparison for different approximation methods.