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Optimized Weight Initialization on the Stiefel Manifold for Deep ReLU Neural Networks
Stable and efficient training of ReLU networks with large depth is highly sensitive to weight initialization. Improper initialization can cause permanent neuron inactivation dying ReLU and exacerbate gradient instability as network depth increases. Methods such as He, Xavier, and orthogonal initiali
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