Decay of correlations and normal approximation for nonstationary heterochaos baker maps

What the math gives to ML

The paper provides a uniform statistical-stability mechanism for deterministic, nonstationary compositions of heterochaos baker maps: despite arbitrary admissible parameter variation, sufficiently regular initial distributions lose memory exponentially fast. Its transferable asset is not the specific baker map, but the construction of a time-varying dynamical system with a sequence-independent mixing rate and a corresponding central-limit approximation. A practical neural analogue is a recurrent or state-space architecture whose transition parameters change during training or across inference time, while an explicit forgetting monitor and constraint enforce a common decay envelope. The key falsifiable prediction is that hidden-state distributions initialized differently should converge at an approximately exponential rate even under parameter schedules unseen during training.