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Poster
L4: Practical loss-based stepsize adaptation for deep learning
Michal Rolinek · Georg Martius

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 210 #64

We propose a stepsize adaptation scheme for stochastic gradient descent. It operates directly with the loss function and rescales the gradient in order to make fixed predicted progress on the loss. We demonstrate its capabilities by conclusively improving the performance of Adam and Momentum optimizers. The enhanced optimizers with default hyperparameters consistently outperform their constant stepsize counterparts, even the best ones, without a measurable increase in computational cost. The performance is validated on multiple architectures including dense nets, CNNs, ResNets, and the recurrent Differential Neural Computer on classical datasets MNIST, fashion MNIST, CIFAR10 and others.

Author Information

Michal Rolinek (Max Planck Institute for Intelligent Systems)
Georg Martius (MPI for Intelligent Systems)

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