Superconvergence - Wikipedia
In numerical analysis, a superconvergent or supraconvergent method is one which converges faster than generally expected (superconvergence or supraconvergence). For example, in the Finite Element Method approximation to Poisson's equation in two dimensions, using piecewise linear elements, the average error in the gradient is first order .
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[1708.07120] Super-Convergence: Very Fast Training of Neural …
2017年8月23日 · In this paper, we describe a phenomenon, which we named "super-convergence", where neural networks can be trained an order of magnitude faster than with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well.
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文章Super-Convergence记录 - CSDN博客
2017年8月31日 · Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates 在这篇文章中，作者针对现在训练较慢，超参数学习率比较难找，给出了自己的解决方案，周期学习率： 将学习率设置一个最大值，和最小值，在给出一个参数stepsize，两个stepsize为一个周期，在前半个stepsize内，学习率_super-convergence: very fast training of neural networks using large learnin.
Superconvergence: How the Genetics, Biotech, and AI Rev…
2024年6月11日 · In Superconvergence , leading futurist and OneShared.World founder Jamie Metzl explores how genome sequencing, gene editing, artificial intelligence, and other technologies are not only changing our lives, but catalyzing each other in …
Super-Convergence: Very Fast Training of Residual Networks Using...
2018年2月15日 · Abstract: In this paper, we show a phenomenon, which we named ``super-convergence'', where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well.
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Abstract - arXiv.org
ng rate. Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet datasets, and resnet, wide-resnet, densenet, and inception archi. ectures. In addition, we show that super-convergence provides a greater boost in performance relative to standard training when the amount of labeled training data is.
Super-convergence: very fast training of neural networks using …
2018年2月15日 · A phenomenon is described, where neural networks can be trained an order of magnitude faster than with standard training methods, and it is shown that super-convergence provides a greater boost in performance relative to standard training when the amount of labeled training data is limited.
Super-convergence: very fast training of neural networks using …
2019年5月10日 · In this paper, we describe a phenomenon, which we named “super-convergence”, where neural networks can be trained an order of magnitude faster than with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well.