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We analyse and explain the increased generalisation performance of iterateaveraging using a Gaussian process perturbation model between the true andbatch risk surface on the high dimensional quadratic. We derive three phenomena\latestEdits{from our theoretical results:} (1) The importance of combiningiterate averaging (IA) with large learning rates and regularisation forimproved regularisation. (2) Justification for less frequent averaging. (3)That we expect adaptive gradient methods to work equally well, or better, withiterate averaging than their non-adaptive counterparts. Inspired by theseresults\latestEdits{, together with} empirical investigations of the importanceof appropriate regularisation for the solution diversity of the iterates, wepropose two adaptive algorithms with iterate averaging. These givesignificantly better results compared to stochastic gradient descent (SGD),require less tuning and do not require early stopping or validation setmonitoring. We showcase the efficacy of our approach on the CIFAR-10/100,ImageNet and Penn Treebank datasets on a variety of modern and classicalnetwork architectures.

Iterative Averaging in the Quest for Best Test Error