An initialization that maximizes a semi-orthogonal weight matrix's alignment with the all-ones vector prevents dying ReLU and keeps 100-layer ReLU networks trainable.
A comparison principle for functions of a uniformly random subspace,
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Optimized Weight Initialization on the Stiefel Manifold for Deep ReLU Neural Networks
An initialization that maximizes a semi-orthogonal weight matrix's alignment with the all-ones vector prevents dying ReLU and keeps 100-layer ReLU networks trainable.