Least squares estimators based on fully connected ReLU networks achieve dimension-free rates for hierarchical composition models, with either logarithmic depth and growing width or fixed width and very large depth.
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On the rate of convergence of fully connected very deep neural network regression estimates
Least squares estimators based on fully connected ReLU networks achieve dimension-free rates for hierarchical composition models, with either logarithmic depth and growing width or fixed width and very large depth.