Deep Layer-wise Networks Have Closed-Form Weights. (arXiv:2202.01210v1 [stat.ML])

There is currently a debate within the neuroscience community over the
likelihood of the brain performing backpropagation (BP). To better mimic the
brain, training a network textit{one layer at a time} with only a “single
forward pass” has been proposed as an alternative to bypass BP; we refer to
these networks as “layer-wise” networks. We continue the work on layer-wise
networks by answering two outstanding questions. First, $textit{do they have a closed-form solution?}$ Second, $textit{how do we know when to stop adding more layers?}$ This work proves that the Kernel Mean Embedding is the
closed-form weight that achieves the network global optimum while driving these
networks to converge towards a highly desirable kernel for classification; we
call it the $textit{Neural Indicator Kernel}$.