The preconditioning exponent p in Adam-like optimizers systematically shifts the relative update ratio between weight and bias parameters, changing which samples the model fits first.
Ussr computational mathematics and mathematical physics4(5), 1–17 (1964)
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Information Allocation Dynamics in Neural Network Optimization
The preconditioning exponent p in Adam-like optimizers systematically shifts the relative update ratio between weight and bias parameters, changing which samples the model fits first.