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How many samples are needed to train a deep neural network?
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Neural networks have become standard tools in many areas, yet many important statistical questions remain open. This paper studies the question of how much data are needed to train a ReLU feed-forward neural network. Our theoretical and empirical results suggest that the generalization error of ReLU feed-forward neural networks scales at the rate $1/\sqrt{n}$ in the sample size $n$ rather than the usual "parametric rate" $1/n$. Thus, broadly speaking, our results underpin the common belief that neural networks need "many" training samples.
Forward citations
Cited by 2 Pith papers
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Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records
A two-step spectral embedding procedure that removes irrelevant components from a knowledge matrix then projects to recover shared and heterogeneous signals for rare-disease clinical concept and patient embeddings.
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How Many Training Samples Are Needed for the Inverse Kinematics Solutions by Artificial Neural Networks
Empirical study on a robotic manipulator concludes that training sets larger than 125 samples yield no further gains in accuracy or efficiency for feedforward neural network inverse kinematics solvers.
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