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We present the surprising result that randomly initialized neural networksare good feature extractors in expectation. These random features correspond tofinite-sample realizations of what we call Neural Network Prior Kernel (NNPK),which is inherently infinite-dimensional. We conduct ablations across multiplearchitectures of varying sizes as well as initializations and activationfunctions. Our analysis suggests that certain structures that manifest in atrained model are already present at initialization. Therefore, NNPK mayprovide further insight into why neural networks are so effective in learningsuch structures.

Learning from Randomly Initialized Neural Network Features