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Ok but it's already known that you shouldn't initialize your network parameters to a single constant and instead initialize the parameters with random numbers.


The model can converge towards such a state even if randomly initialized.


Both you and the comment above are correct; initializing with iid elements ensures that correlations are not disastrous for training, but strong correlations are baked into the weights during training, so pretty much anything could potentially happen.




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