A data-parallel trust-region optimizer (APTS) achieves validation accuracy comparable to fine-tuned Adam on MNIST and CIFAR-10 with fixed hyperparameters.
SIAM Journal on Scientific Computing, 24, 183–200 (2002)
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Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
A data-parallel trust-region optimizer (APTS) achieves validation accuracy comparable to fine-tuned Adam on MNIST and CIFAR-10 with fixed hyperparameters.