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Several papers argue that wide minima generalize better than narrow minima.In this paper, through detailed experiments that not only corroborate thegeneralization properties of wide minima, we also provide empirical evidencefor a new hypothesis that the density of wide minima is likely lower than thedensity of narrow minima. Further, motivated by this hypothesis, we design anovel explore-exploit learning rate schedule. On a variety of image and naturallanguage datasets, compared to their original hand-tuned learning ratebaselines, we show that our explore-exploit schedule can result in either up to0.84% higher absolute accuracy using the original training budget or up to 57%reduced training time while achieving the original reported accuracy. Forexample, we achieve state-of-the-art (SOTA) accuracy for IWSLT'14 (DE-EN)dataset by just modifying the learning rate schedule of a high performingmodel.

Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule