Asymptotics of Learning with Deep Structured (Random) Features
Dominik Schröder Daniil Dmitriev Hugo Cui Bruno Loureiro
For a large class of feature maps we provide a tight asymptotic
characterisation of the test error associated with learning the readout layer,
in the high-dimensional limit where the input dimension, hidden layer widths,
and number of training samples are proportionally large. This characterization
is formulated in terms of the population covariance of the features. Our work
is partially motivated by the problem of learning with Gaussian rainbow neural
networks, namely deep non-linear fully-connected networks with random but
structured weights, whose row-wise covariances are further allowed to depend on
the weights of previous layers. For such networks we also derive a closed-form
formula for the feature covariance in terms of the weight matrices. We further
find that in some cases our results can capture feature maps learned by deep,
finite-width neural networks trained under gradient descent.