Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
This work addresses the problem of efficient parallel training of deep neural networks for machine learning practitioners and researchers.
The authors tackled the problem of parallel training of deep neural networks and achieved comparable validation accuracy to SGD and Adam on MNIST and CIFAR-10 datasets, while eliminating the need for costly hyperparameter tuning. The proposed method allows for parallel training and achieves similar accuracy to existing methods.
Parallel training methods are increasingly relevant in machine learning (ML) due to the continuing growth in model and dataset sizes. We propose a variant of the Additively Preconditioned Trust-Region Strategy (APTS) for training deep neural networks (DNNs). The proposed APTS method utilizes a data-parallel approach to construct a nonlinear preconditioner employed in the nonlinear optimization strategy. In contrast to the common employment of Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam), which are both variants of gradient descent (GD) algorithms, the APTS method implicitly adjusts the step sizes in each iteration, thereby removing the need for costly hyperparameter tuning. We demonstrate the performance of the proposed APTS variant using the MNIST and CIFAR-10 datasets. The results obtained indicate that the APTS variant proposed here achieves comparable validation accuracy to SGD and Adam, all while allowing for parallel training and obviating the need for expensive hyperparameter tuning.