How Many Neurons Does it Take to Approximate the Maximum?
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We study the size of a neural network needed to approximate the maximum function over d inputs, in the most basic setting of approximating with respect to the L_2 norm, for continuous distributions, for a network that uses ReLU activations. We provide new lower and upper bounds on the width required for approximation across various depths. Our results establish new depth separations between depth 2 and 3, and depth 3 and 5 networks, as well as providing a depth πͺ(log(log(d))) and width πͺ(d) construction which approximates the maximum function, significantly improving upon the depth requirements of the best previously known bounds for networks with linearly-bounded width. Our depth separation results are facilitated by a new lower bound for depth 2 networks approximating the maximum function over the uniform distribution, assuming an exponential upper bound on the size of the weights. Furthermore, we are able to use this depth 2 lower bound to provide tight bounds on the number of neurons needed to approximate the maximum by a depth 3 network. Our lower bounds are of potentially broad interest as they apply to the widely studied and used max function, in contrast to many previous results that base their bounds on specially constructed or pathological functions and distributions.
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