We introduce CompleteP, which offers depth-wise HP transfer, FLOP savings when training deep models, and a larger range of compute-efficient width/depth ratios.

We introduce $\mu$PC, a reparameterisation of predictive coding networks that enables stable training of 100+ layer ResNets on simple tasks with hyperparameter transfer.