First of all, great blog post with great examples. Reminds me of distill.pub used to be.

Second, the article correctly states that typically L2 weight decay is used, leading to a lot of weights with small magnitudes. For models that generalize better, would it then be better to always use L1 weight decay to promote sparsity in combination with longer training?

I wonder whether deep learning models that only use sparse fourier features rather than dense linear layers would work better...