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Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos
We ask whether the standard treatment of dropout as a static hyperparameter is optimal, or whether its utility can be improved by letting it vary over depth. We answer this by developing a mean-field theory of dropout near the edge of chaos, identifying distinct universality classes for smooth and k
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