Derives exact Frobenius norm imbalance identity for deep nonlinear networks, classifies activations into four classes, and obtains critical-depth escape time law τ★ = Θ(ε^{-(r-2)}) from reduction to scalar ODE on permutation-symmetric submanifold.
Annual Conference Computational Learning Theory , year =
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Repeating smaller datasets speeds up training via sampling biases that enable appropriate layer-wise growth, leading to compute savings over larger datasets across tasks and architectures.
Generative models learn rules before memorizing data, creating an innovation window whose width depends on dataset size and rule complexity, observed in both diffusion and autoregressive architectures.
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