class number changes or simply varying the parameterization combined with algorithm changes the inductive bias here and here you can characterize in a very precise sense but whatever gradient can descend converges to is a stationary point of some problem with this shot and quasi norm okay a similar phenomenon in convolutional networks same thing you can take a linear convolutional net and write it as of comp of predictors you'll also get a quasi norm except now it's sparsely instead of on the singular values okay