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We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have.
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On the number of inference regions of deep feed forward networks with piece-wise linear activations
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Revisiting natural gradient for deep networks
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