A Spline Theory of Deep Learning

ICML 2018 Randall Balestrierobaraniuk

We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of max-affine spline operators (MASOs), which provide a powerful portal through which to view and analyze their inner workings... (read more)

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