Aug 13, 2016 · In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks.

Feb 6, 2017 · In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks.

In this paper, we propose to periodically simulate warm restarts of SGD, where in each restart the learning rate is initialized to some value and is scheduled ...

Jul 25, 2019 · "SGDR: Stochastic Gradient Descent with Warm Restarts." Ilya Loshchilov, Frank Hutter (2017) mirror Dagstuhl Trier

May 3, 2017 · In this paper, we propose to periodically simulate warm restarts of SGD, where in each restart the learning rate is initialized to some value ...

This paper proposes a simple restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks.

The first technique is Stochastic Gradient Descent with Restarts (SGDR), a variant of learning rate annealing, which gradually decreases the learning rate ...

Aug 20, 2016 · In this paper, we propose a simple restart technique for stochastic gradient descent to improve its anytime performance when training deep ...

Mar 8, 2021 · In this article, we will dive into the concept of Stochastic Gradient Descent with Warm Restarts in deep learning optimization and training.

Lasagne implementation of SGDR on WRNs from "SGDR: Stochastic Gradient Descent with Restarts" by Ilya Loshchilov and Frank Hutter.