Deep linear network theory derives logarithmic decay for cross-entropy loss under gap-growth conditions versus polynomial closure for Schatten-regularized structural energy under late-time KL tails, separating fitting from simplification; conditional reductions extend this to ReLU MLPs with fixed ac
arXiv preprint arXiv:2502.17340 , year =
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Deciphering Two Training Clocks in Grokking via Deep Linear Network Theory with Conditional ReLU Reduction
Deep linear network theory derives logarithmic decay for cross-entropy loss under gap-growth conditions versus polynomial closure for Schatten-regularized structural energy under late-time KL tails, separating fitting from simplification; conditional reductions extend this to ReLU MLPs with fixed ac