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• W2892676835 abstract "In this paper, we explore some basic questions on the complexity of training neural networks with ReLU activation function. We show that it is NP-hard to train a two-hidden layer feedforward ReLU neural network. If dimension of the input data and the network topology is fixed, then we show that there exists a polynomial time algorithm for the same training problem. We also show that if sufficient over-parameterization is provided in the first hidden layer of ReLU neural network, then there is a polynomial time algorithm which finds weights such that output of the over-parameterized ReLU neural network matches with the output of the given data." @default.
• W2892676835 created "2018-10-05" @default.
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• W2892676835 date "2018-09-27" @default.
• W2892676835 modified "2023-09-26" @default.
• W2892676835 title "Complexity of Training ReLU Neural Network" @default.
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• W2892676835 doi "https://doi.org/10.48550/arxiv.1809.10787" @default.
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