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[D] Self normalizing weight and activations

I need to train a classifier and use the last linear layer as embedding for other stuff. If possible I want the weights always constrained to -1 and 1, N(0,1), during training.

Is there paper that shows method to update weights so that all weights have 0 mean and 1 variance? Does the function weight_norm in pytorch actually does that?

I read paper Self Normalizing Neural Networks but only activations are normalized to N(0, 1)

And speaking of self normalizing models, does anyone made a network that explicitly output values already approximately close to softmax without calculating softmax with logits?

Thanks

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.