hi we have some AV issues we'll start shortly

I think kernel is a fixed function of whatever dataset you have, independent of the data. To represent it as a matrix you’d need ~N^2 numbers, for N^2 datapoints. But then incorporating finite width corrections, you’d need N^4 numbers.

To Lucas question: yes since you have expressions for the whole ensemble, you can compute the mean behavior. Though the perturbation theory breaks down for for L/n ~ 1. Sho is pointing out that in this regime where perturbation theory breaks down, any network is typically untrainable, and you would need an unrealistic number of realizations in your ensemble to see the mean behavior.

You can evaluate these equations efficiently for small datasets and check, yes.

Have people done it?

We evaluated them to convince ourselves that what we did is correct. But there’s no formal study in the literature.

Ok thanks!

I have to run on the hour, but thanks for the really interesting talk!

great talk