Data-dependent Initializations of Convolutional Neural Networks

Efficient backprop

LeCun, Y., Bottou, L., Orr, G., and Muller, K · 1998

Earlier work this paper cites.

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Bradley, David M · 2010

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Understanding the difficulty of training deep feedforward neural networks

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Earlier work this paper cites.

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Earlier work this paper cites.

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