Sample Variance Decay in Randomly Initialized ReLU Networks

13 Feb 2019Kyle LutherH. Sebastian Seung

Before training a neural net, a classic rule of thumb is to randomly initialize the weights so the variance of activations is preserved across layers. This is traditionally interpreted using the total variance due to randomness in both weights \emph{and} samples... (read more)

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