Runge-Kutta optimizers adapted with momentum, preconditioning, or adaptive learning rates can close the large-batch generalization gap and match Adam on small MLP workloads.
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Learning by solving differential equations
Runge-Kutta optimizers adapted with momentum, preconditioning, or adaptive learning rates can close the large-batch generalization gap and match Adam on small MLP workloads.