Kingma & Ba, "Adam: A Method for Stochastic Optimization" (arXiv)
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In plain words
A new algorithm called Adam finds good solutions to huge noisy problems by tracking running averages of gradients and their sizes so steps adjust themselves with almost no tuning. It suits large data or many parameters, noisy or sparse signals, uses little memory and compute, works well in tests against other methods, and comes with a matching best-known convergence guarantee. It proposes the method with links to earlier algorithms it builds on, theoretical analysis, empirical comparisons, and a simple variant.
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Diederik P. Kingma, Jimmy Ba did not write this page.
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