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Tripling the number of training iterations () effectively resolves the degradation problem in deep plain networks.
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Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.3 Deep Residual Network Architecture - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
The Degradation Problem in Deep Networks - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
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What empirical observation demonstrates that the degradation problem in deep plain networks trained with batch normalization is not caused by vanishing or exploding signals?
Tripling the number of training iterations () effectively resolves the degradation problem in deep plain networks.
What specific form of convergence rate impedes training error reduction in deep plain networks suffering from the degradation problem?
Contrast the challenge standard optimizers face when an identity mapping is optimal in deep plain networks versus when using an explicit identity shortcut.